Rescale

rescale

Authors:
(1) Luyuan Peng, Acoustic Research Laboratory, National University of Singapore;
(2) Hari Vishnu, Acoustic Research Laboratory, National University of Singapore;
(3) Mandar Chitre, Acoustic Research Laboratory, National University of Singapore;
(4) Yuen Min Too, Acoustic Research Laboratory, National University of Singapore;
(5) Bharath Kalyan, Acoustic Research Laboratory, National University of Singapore;
(6) Rajat Mishra, Acoustic Research Laboratory, National University of Singapore. Table of Links I Introduction II Method III Datasets IV Experiments, Acknowledgment, and References II. METHOD We implement two different architectures. The first uses pretrained deep convolutional neural networks (DCNN) to extract features from the input images. The resulting feature map is then passed to an affine regressor consisting of dense neural layers to output a 7-dimensional pose vector estimates. The second architecture adds an additional LSTM layer between the DCNN and affine regressor to perform dimensionality reduction by assessing the spatial structure of the image. This is done by four LSTMs which parse the DCNN-output feature map in different directions starting at each of the four image corners to process the spatial trends, and compress it into lower-dimensional information which is easier processed by the affine regressor. The input images used in the training are rescaled to 256×256 pixels before cropping into a 224×224 feature input using centre cropping. To speed up training, the images are normalized by the mean and standard deviation of the images. The poses are also normalized to lie within [-1, 1]. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Luyuan Peng, Acoustic Research Laboratory, National University of Singapore; (2) Hari Vishnu, Acoustic Research Laboratory, National University of Singapore; (3) Mandar Chitre, Acoustic Research Laboratory, National University of Singapore; (4) Yuen Min Too, Acoustic Research Laboratory, National University of Singapore; (5) Bharath Kalyan, Acoustic Research Laboratory, National University of Singapore; (6) Rajat Mishra, Acoustic Research Laboratory, National University of Singapore. Authors: Authors: (1) Luyuan Peng, Acoustic Research Laboratory, National University of Singapore; (2) Hari Vishnu, Acoustic Research Laboratory, National University of Singapore; (3) Mandar Chitre, Acoustic Research Laboratory, National University of Singapore; (4) Yuen Min Too, Acoustic Research Laboratory, National University of Singapore; (5) Bharath Kalyan, Acoustic Research Laboratory, National University of Singapore; (6) Rajat Mishra, Acoustic Research Laboratory, National University of Singapore. Table of Links I Introduction I Introduction II Method II Method III Datasets III Datasets IV Experiments, Acknowledgment, and References IV Experiments, Acknowledgment, and References II. METHOD We implement two different architectures. The first uses pretrained deep convolutional neural networks (DCNN) to extract features from the input images. The resulting feature map is then passed to an affine regressor consisting of dense neural layers to output a 7-dimensional pose vector estimates. The second architecture adds an additional LSTM layer between the DCNN and affine regressor to perform dimensionality reduction by assessing the spatial structure of the image. This is done by four LSTMs which parse the DCNN-output feature map in different directions starting at each of the four image corners to process the spatial trends, and compress it into lower-dimensional information which is easier processed by the affine regressor. The input images used in the training are rescaled to 256×256 pixels before cropping into a 224×224 feature input using centre cropping. To speed up training, the images are normalized by the mean and standard deviation of the images. The poses are also normalized to lie within [-1, 1]. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Underwater Visual Localization Using Machine Learning and LSTM: Method

HDR or SDR? A Study of Scaled and Compressed Videos: Objective Assessment

Authors:
(1) Juan F. Montesinos, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {juanfelipe.montesinos@upf.edu};
(2) Olga Slizovskaia, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {olga.slizovskaia@upf.edu};
(3) Gloria Haro, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {gloria.haro@upf.edu}. Table of Links Abstract and Intro
Related work
Dataset
Experiments
Conclusions and References IV. EXPERIMENTS In order to show the suitability of Solos, we have focused in the blind source separation problem and have trained The Sound of Pixels (SoP) [23] and the Multi-head U-Net (MHUNet) [34] models on the new dataset. We have carried out four experiments: i) we have evaluated the SoP pre-trained model provided by the authors; ii) we have trained SoP from scratch; iii) we have fine-tuned SoP on Solos starting from the weights of the pre-trained model on MUSIC and iv) we have trained the Multi-head U-Net from scratch. MHU-Net has been trained to separate mixtures with the number of sources varied from two to seven following a curriculum learning procedure as it improves the results. SoP has been trained according to the optimal strategy described in [23]. Evaluation is performed on the URMP dataset [1] using the real mixtures they provide. URMP tracks are sequentially split in 6s-duration segments. Metrics are obtained from all the resulting splits. A. Architectures and training details We have chosen The Sound of Pixels as baseline since its weights are publicly available and the network is trained in a straight-forward way. SoP is composed of three main subnetworks: A dilated ResNet [35] as video-analysis network, a U-Net [36] as audio-processing network and an audio synthesizer network. We also compare its results against a Multi-head U-Net [34]. U-Net [37] is an encoder-decoder architecture with skip connections in between. Skip connections help to recover the original spatial structure. MHU-Net is a step forward as it consist of as many decoders as possible sources. Each decoder is specialized in a single source, thus improving performance. The Sound of Pixels [23] does not follow the original UNet architecture proposed for biomedical imaging, but the UNet described at [36], which was tuned for singing voice separation. Instead of having two convolutions per block followed by max-pooling, they use a single convolution with a bigger kernel and striding. The original work proposes a central block with learnable parameters whereas the central block is a static latent space in SoP. U-Net has been widely used as backbone of several architectures for tasks such us image generation [38], noise suppression and super-resolution [39], image-to-image translation [40], image segmentation [37] or audio source separation [36]. SoP U-Net consists of 7 blocks with 32, 64, 128, 256, 512, 512 and 512 channels respectively (6 blocks for the MHU-Net). The latent space can be considered as the last output of the encoder. Dilated ResNet is a ResNet-like architecture which makes use of dilated convolutions to keep the receptive field while increasing the resulting spatial resolution. The output of the U-Net is a set of 32 spectral components (channels) which are the same size than the input spectrogram, in case of SoP, and a single source per decoder in case of MHU-Net. Given a representative frame, visual features are obtained using the Dilated ResNet. These visual features are nothing but a vector of 32 elements (which corresponds to the number of output channels of UNet) which are used to select proper spectral components. This selection is performed by the audio analysis network which consist of 32 learnable parameters, αk, plus a bias, β. This operation can be mathematically described as follows: where Sk(t, f) is the k-th predicted spectral component at time-frequency bin (t, f). Figure 2 illustrates the SoP configuration. It is interesting to highlight that making the visual network to select the spectral components forces it to indirectly learn instrument localization, which can be inferred via activation maps. Ground-truth mask calculation for both SoP and MHU-Net are described in Eq. (2) and Eq. (3), Sec. IV-C. B. Data pre-processing In order to train the aforementioned architectures, audio is re-sampled to 11025 Hz and 16 bit. Samples fed into the network are 6s duration. We use Short-time Fourier Transform (STFT) to obtain time-frequency representations of waveforms. Following [23], STFT is computed using Hanning window of length 1022 and hop length 256 so that we obtain a spectrogram of size 512×256 for a 6s sample. Later on, we apply a log re-scale on the frequency axis expanding lower frequencies and compressing higher ones. Lastly, we convert magnitude spectrograms into dB w.r.t. the minimum value of each spectrogram and normalize between -1 and 1. C. Ground-truth mask Before introducing ground-truth mask computations we would like to point out some considerations. Standard floatingpoint audio format imposes a waveform to be bounded between -1 and 1. At the time of creating artificial mixtures resulting waveforms may be out of these bounds. This can help neural networks to find shortcuts to overfit. To avoid this behaviour spectrograms are clamped according to the equivalent bounds in the time-frequency domain. The Discrete Short-time Fourier Transform can be computed as described in [42]: For training Sound of Pixels we have used complementary binary masks as ground-truth masks, defined as: The Multi-head U-Net has been trained with complementary ratio masks, defined as: D. Results Benchmark results for Source to Distortion Ratio (SDR), Source to Interferences Ratio (SIR), Sources to Artifacts Ratio (SAR) proposed in [43] are shown in Table II in terms of mean and standard deviation. As it can be observed, Sound of Pixels evaluated using its original weights performs the worst. One possible reason for that could be the absence of some of the URMP categories on the MUSIC dataset. If we train the network from scratch on Solos, results improve by almost 1 dB. However, it is possible to achieve an even better result fine-tuning the network, pre-trained with MUSIC, on Solos. We hypothesize that the improvement occurs as the network is exposed to much more training data. Moreover, the table results show how it is possible to reach higher performance by using more powerful architectures like MHU-Net. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Juan F. Montesinos, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {juanfelipe.montesinos@upf.edu}; (2) Olga Slizovskaia, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {olga.slizovskaia@upf.edu}; (3) Gloria Haro, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {gloria.haro@upf.edu}. Authors: Authors: (1) Juan F. Montesinos, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {juanfelipe.montesinos@upf.edu}; (2) Olga Slizovskaia, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {olga.slizovskaia@upf.edu}; (3) Gloria Haro, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {gloria.haro@upf.edu}. Table of Links Abstract and Intro Related work Dataset Experiments Conclusions and References Abstract and Intro Abstract and Intro Related work Related work Dataset Dataset Experiments Experiments Conclusions and References Conclusions and References IV. EXPERIMENTS In order to show the suitability of Solos, we have focused in the blind source separation problem and have trained The Sound of Pixels (SoP) [23] and the Multi-head U-Net (MHUNet) [34] models on the new dataset. We have carried out four experiments: i) we have evaluated the SoP pre-trained model provided by the authors; ii) we have trained SoP from scratch; iii) we have fine-tuned SoP on Solos starting from the weights of the pre-trained model on MUSIC and iv) we have trained the Multi-head U-Net from scratch. MHU-Net has been trained to separate mixtures with the number of sources varied from two to seven following a curriculum learning procedure as it improves the results. SoP has been trained according to the optimal strategy described in [23]. Evaluation is performed on the URMP dataset [1] using the real mixtures they provide. URMP tracks are sequentially split in 6s-duration segments. Metrics are obtained from all the resulting splits. A. Architectures and training details A. Architectures and training details We have chosen The Sound of Pixels as baseline since its weights are publicly available and the network is trained in a straight-forward way. SoP is composed of three main subnetworks: A dilated ResNet [35] as video-analysis network, a U-Net [36] as audio-processing network and an audio synthesizer network. We also compare its results against a Multi-head U-Net [34]. U-Net [37] is an encoder-decoder architecture with skip connections in between. Skip connections help to recover the original spatial structure. MHU-Net is a step forward as it consist of as many decoders as possible sources. Each decoder is specialized in a single source, thus improving performance. The Sound of Pixels [23] does not follow the original UNet architecture proposed for biomedical imaging, but the UNet described at [36], which was tuned for singing voice separation. Instead of having two convolutions per block followed by max-pooling, they use a single convolution with a bigger kernel and striding. The original work proposes a central block with learnable parameters whereas the central block is a static latent space in SoP. U-Net has been widely used as backbone of several architectures for tasks such us image generation [38], noise suppression and super-resolution [39], image-to-image translation [40], image segmentation [37] or audio source separation [36]. SoP U-Net consists of 7 blocks with 32, 64, 128, 256, 512, 512 and 512 channels respectively (6 blocks for the MHU-Net). The latent space can be considered as the last output of the encoder. Dilated ResNet is a ResNet-like architecture which makes use of dilated convolutions to keep the receptive field while increasing the resulting spatial resolution. The output of the U-Net is a set of 32 spectral components (channels) which are the same size than the input spectrogram, in case of SoP, and a single source per decoder in case of MHU-Net. Given a representative frame, visual features are obtained using the Dilated ResNet. These visual features are nothing but a vector of 32 elements (which corresponds to the number of output channels of UNet) which are used to select proper spectral components. This selection is performed by the audio analysis network which consist of 32 learnable parameters, αk, plus a bias, β. This operation can be mathematically described as follows: where Sk(t, f) is the k-th predicted spectral component at time-frequency bin (t, f). Figure 2 illustrates the SoP configuration. It is interesting to highlight that making the visual network to select the spectral components forces it to indirectly learn instrument localization, which can be inferred via activation maps. Ground-truth mask calculation for both SoP and MHU-Net are described in Eq. (2) and Eq. (3), Sec. IV-C. B. Data pre-processing B. Data pre-processing In order to train the aforementioned architectures, audio is re-sampled to 11025 Hz and 16 bit. Samples fed into the network are 6s duration. We use Short-time Fourier Transform (STFT) to obtain time-frequency representations of waveforms. Following [23], STFT is computed using Hanning window of length 1022 and hop length 256 so that we obtain a spectrogram of size 512×256 for a 6s sample. Later on, we apply a log re-scale on the frequency axis expanding lower frequencies and compressing higher ones. Lastly, we convert magnitude spectrograms into dB w.r.t. the minimum value of each spectrogram and normalize between -1 and 1. C. Ground-truth mask C. Ground-truth mask Before introducing ground-truth mask computations we would like to point out some considerations. Standard floatingpoint audio format imposes a waveform to be bounded between -1 and 1. At the time of creating artificial mixtures resulting waveforms may be out of these bounds. This can help neural networks to find shortcuts to overfit. To avoid this behaviour spectrograms are clamped according to the equivalent bounds in the time-frequency domain. The Discrete Short-time Fourier Transform can be computed as described in [42]: For training Sound of Pixels we have used complementary binary masks as ground-truth masks, defined as: The Multi-head U-Net has been trained with complementary ratio masks, defined as: D. Results D. Results Benchmark results for Source to Distortion Ratio (SDR), Source to Interferences Ratio (SIR), Sources to Artifacts Ratio (SAR) proposed in [43] are shown in Table II in terms of mean and standard deviation. As it can be observed, Sound of Pixels evaluated using its original weights performs the worst. One possible reason for that could be the absence of some of the URMP categories on the MUSIC dataset. If we train the network from scratch on Solos, results improve by almost 1 dB. However, it is possible to achieve an even better result fine-tuning the network, pre-trained with MUSIC, on Solos. We hypothesize that the improvement occurs as the network is exposed to much more training data. Moreover, the table results show how it is possible to reach higher performance by using more powerful architectures like MHU-Net. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Solos: A Dataset for Audio-Visual Music Analysis -  Experiments

একক: অডিও-ভিজ্যুয়াল সঙ্গীত বিশ্লেষণের জন্য একটি ডেটাসেট - পরীক্ষা-নিরীক্ষা

Solos: Ein Datensatz zur audiovisuellen Musikanalyse - Experimente

Solos: un conjunto de datos para el análisis de música audiovisual: experimentos

Solos : un ensemble de données pour l'analyse de la musique audiovisuelle - Expériences

सोलोस: ऑडियो-विज़ुअल संगीत विश्लेषण के लिए एक डेटासेट - प्रयोग

Solos: オーディオビジュアル音楽分析のためのデータセット - 実験

Solos: 시청각 음악 분석을 위한 데이터 세트 - 실험

Solos: um conjunto de dados para análise musical audiovisual - experimentos

Соло: набор данных для аудиовизуального музыкального анализа — эксперименты

Sololar: Görsel-İşitsel Müzik Analizi için Bir Veri Kümesi - Deneyler

Solos: Bộ dữ liệu để phân tích âm nhạc nghe nhìn - Thử nghiệm

Solos：视听音乐分析数据集 - 实验

VEATIC: Familiarity and Enjoyment Ratings & References

VEATIC: পরিচিতি এবং উপভোগ রেটিং এবং রেফারেন্স

VEATIC: Vertrautheits- und Genussbewertungen und Referenzen

VEATIC: Calificaciones y referencias de familiaridad y disfrute

VEATIC : Notes et références de familiarité et de plaisir

VEATIC: परिचितता और आनंद रेटिंग और संदर्भ

VEATIC: 親しみやすさと楽しさの評価と参考文献

VEATIC: 친숙함과 즐거움 평가 및 참고자료

VEATIC: classificações e referências de familiaridade e prazer

VEATIC: рейтинги знакомства и удовольствия, а также ссылки

VEATIC: Aşinalık ve Eğlence Derecelendirmeler ve Referanslar

VEATIC: Đánh giá và tham khảo về sự quen thuộc và thích thú

VEATIC：熟悉度和享受度评级及参考

Authors: (1) Jonathan H. Rystrøm. Table of Links Abstract and Introduction Previous Literature Methods and Data Results Discussions Conclusions and References A. Validation of Assumptions B. Other Models C. Pre-processing steps B Other Models B.1 Model without activity-level However, when we ran the model (also using lmerTest (Kuznetsova et al., 2017)) and plotted the residuals, we got the following: Just from a brief visual inspection, it is clear to see that the residuals are not randomly distributed: There are two distinct ”bands” that both seem to trend upward. This breaks the assumption that the residuals are randomly distributed (Poole & O’Farrell, 1971). While many assumption violations are reduced with enough data (Baayen et al., 2008; Schielzeth et al., 2020), non-linearity is not one of them (Poole & O’Farrell, 1971). Fortunately, the non-random residuals were (partially) fixed by introducing activity-level for the reasons described in section 3.3. B.2 Problematic Categories Removed Here we fit the main model (eq. 5, with problematic categories removed. We define a problematic category as a category with a fitted random effect of less than -0.5. We obtain this threshold by visually inspecting Fig. 7. The results of this fit can be seen below in 2: B.3 Gamma mixed-effects model In the following, we refit the main model (eq. 5) using a Gamma regression. This is the most widely recommended solution in the literature on fitting right-tailed, heteroscedastic outcomes (Feng et al., 2014; Villadsen & Wulff, 2021). However, since we discovered this after running our initial models, we could only justify doing this as a post-hoc test. I use the lme4-package to fit the model (Bates et al., 2014). To avoid convergence errors and adapt the model to the formula, we make the following alterations: Add a gamma log-link function (Fox, 2015) change the year (β1) estimate to decades. This has the effect of rescaling the effect size. We still log-transform the activity-level to rescale it. As this is not part of the hypothesis, this does not affect our interpretation. This leads us to the interpretation. The effect size per decade is 0.31, which is highly significant (SE=0.006, T=24, p ≪ 0.001). This translates into 34% increase per decade or 3% increase per year. This is the same direction as our transformed linear model, with somewhat larger results. However, as these come from an almost unidentifiable fit with extremely non-random residuals, no inferences can be drawn from this. Some of this indicates that the conditional distribution of Barrier-to-Exit is not a Gamma distribution. Pursuing the GLMM path would require further assessments of the best-fitting distribution. This could be by e.g. applying the Box-Cox method as described by Villadsen and Wulff (2021). This paper is under CC 4.0 license. available on arxiv

Assessing Non-linearities and Distribution Assumptions in Barrier-to-Exit Analysis

This paper is available on arxiv under CC 4.0 license. Authors: (1) Pierre Colombo, Equall, Paris, France & MICS, CentraleSupelec, Universite Paris-Saclay, France; (2) Victor Pellegrain, IRT SystemX Saclay, France & France & MICS, CentraleSupelec, Universite Paris-Saclay, France; (3) Malik Boudiaf, ÉTS Montreal, LIVIA, ILLS, Canada; (4) Victor Storchan, Mozilla.ai, Paris, France; (5) Myriam Tami, MICS, CentraleSupelec, Universite Paris-Saclay, France; (6) Ismail Ben Ayed, ÉTS Montreal, LIVIA, ILLS, Canada; (7) Celine Hudelot, MICS, CentraleSupelec, Universite Paris-Saclay, France; (8) Pablo Piantanida, ILLS, MILA, CNRS, CentraleSupélec, Canada. Table of Links Abstract & Introduction Related Work API-based Few-shot Learning An Enhanced Experimental Setting Experiments Conclusions Limitations, Acknowledgements, & References Appendix A: Proof of Proposition Appendix B: Additional Experimental Results 2 Related Work 2.1 Few-shot learning in NLP Numerous studies have tackled the task of FSL in Natural Language Processing (NLP) by utilizing pre-trained language models (Devlin et al., 2018; Liu et al., 2019b; Radford et al., 2019; Yang et al., 2019). These methods can be classified into three major categories: prompt-based, parameter efficient tuning, and in-context learning. Prompt-based FSL: Prompt-based FSL involves the use of natural language prompts or templates to guide the model to perform a specific task (Ding et al., 2021; Liu et al., 2023). For example, the seminal work (Schick and Schütze, 2020a) proposed a model called PET, which uses a pre-defined set of prompts to perform various NLP tasks as text classification. They also impose a choice of a verbalizer which highly impacts the classification performances (Cui et al., 2022; Hu et al., 2021a). However, recent studies have questioned the benefits of prompt-based learning due to the high variability in performance caused by the choice of prompt (Liu et al., 2022). To address this issue, researchers have proposed prompt tuning which involves a few learnable parameters in addition to the prompt (Lester et al., 2021). Nevertheless, these approaches face limitations when learning from API: (i) encoder access for gradient computation is infeasible (as in R1), (ii) prompting requires to send data and label which raises privacy concerns (as in R3), and (iii) labeling new points is time-consuming (see in R3) and expensive due to the need to send all shots for each input token[1]. These methods, such as adapters (Houlsby et al., 2019; Pfeiffer et al., 2020), keep most of the model’s parameters fixed during training and only update small feedforward networks that are inserted within the larger model architecture. A recent example is T-FEW (Liu et al., 2022), which adds learned vectors that rescale the network’s internal activations. Additionally, it requires a set of manually created prompts for each dataset making it hard to use in practice. Relying on parameter-efficient fine-tuning methods with an API is not possible due to the need to compute gradients of the encoder (as per R1) and the requirement to send both the labeling schema and the labels, which violates R3. Parameter-efficient fine-tuning. In-context learning models are models that utilize input-to-output training examples as prompts to make predictions, without any parameter updates (Wei et al., 2022). These models, such as text-davinci, rely solely on the provided examples to generate predictions, without any additional training. However, a significant drawback of this approach is that the user must supply the input, label examples, and task description, which becomes prohibitively expensive when the number of classes or shots increases, is slow (Liu et al., 2022) (R2) and raises data privacy concerns (as highlighted in R3). Additionally, the inability to reuse text embeddings for new tasks or with new labels without querying the model’s API limits practicality and scalability, making reusable encoding unfeasible for in-context learning models [2]. In Context Learning (ICL). or quite long stood as the de-facto paradigm for FSL ((Snell et al., 2017; Rusu et al., 2019; Sung et al., 2018b; Lee et al., 2019; Raghu et al., 2019; Sun et al., 2019a)). In meta-learning, the objective is to provide the model with the intrinsic ability to learn in a data-efficient manner. For instance, MAML ((Finn et al., 2017a; Antoniou et al., 2018)), arguably the most popular meta-learning method, tries to train a model such that it can be fine-tuned end-to-end using only a few supervised samples while retaining high generalization ability. Unlike the three previous lines of work, meta-learning methods operate by modifying the pre-training procedure and therefore assume access to both the training data and the model, which wholly breaks both R1 and R3. 2.2 Inductive vs transductive learning Learning an inductive classifier on embeddings generated by an API-based model, as proposed by (Snell et al., 2017), is a common baseline for performing FSL. This approach is prevalent in NLP, where a parametric model is trained on data to infer general rules that are applied to label new, unseen data (known as inductive learning (Vapnik, 1999)). However, in FSL scenarios with limited labeled data, this approach can be highly ambiguous and lead to poor generalization. Transduction offers an attractive alternative to inductive learning (Sain, 1996). Unlike inductive learning, which infers general rules from training data, transduction involves finding rules that work specifically for the unlabeled test data. By utilizing more data, such as unlabeled test instances, and aiming for a more localized rule rather than a general one, transductive learning has shown promise and practical benefits in computer vision (Boudiaf et al., 2020, 2021; Ziko et al., 2020). Transductive methods yield substantially better performance than their inductive counterparts by leveraging the statistics of the query set (Dhillon et al., 2019). However, this approach has not yet been explored in the context of textual data. [1] The cost of API queries is determined by the number of input tokens that are transmitted. [2] Furthermore, as the number of considered classes increases, the fixed size of the transformer limits the number of possible shots that can be fed to the model. Previous studies have often neglected this limitation by focusing on a few numbers of labels.

Transductive Learning for Textual Few-Shot Classification: Related Work

Have you ever wondered how your face would look as a whimsical cartoon or how you could magically change your hair color in a photograph? The cutting-edge technology we're about to explore can make those dreams a reality and take your images to dazzling new heights! 🎨🚀 This most recent video is about a revolutionary AI architecture known as StyleGANEX. Developed by brilliant researchers and showcased at ICCV 2023, StyleGANEX is all about pushing boundaries and shattering limitations. No more settling for pixelated or low-res images! StyleGANEX elegantly tackles the challenges of flexible input sizes, allowing you to transform any image into a stunning work of art without compromising on quality. 🌈 And the best thing about it is that their code is fully open-source, and they have a demo available online (all links in the video description)! Obviously, the architecture is mostly based on StyleGAN, which has lots of pre-trained models you can play with, but all have this input image size issue we discussed, which ends up producing different encodings in the latent space too since they all follow the same convolutions. So, everything is of different sizes based on the image you want to process. Here, the researchers did a simple fix to this issue by implementing a dilated convolution to rescale the receptive fields of shallow layers in the StyleGAN generator architecture. But what does this mean? I'll guide you through the power of dilated convolutions, the wizardry that makes StyleGANEX an absolute game-changer in the world of AI image editing. And the best part? You don't need to be a tech expert to understand it all! 🧙🎓 Don't miss out – hit that play button and immerse yourself in the magic of StyleGANEX. And hey, if you're attending ICCV 2023 in Paris, we might cross paths! 🗼 https://youtu.be/Xj20I2zFYi0?embedable=true References: ►Read the full article: https://www.louisbouchard.ai/styleganex/ ►Demo: https://huggingface.co/spaces/PKUWilliamYang/StyleGANEX ►Paper: https://arxiv.org/abs/2303.06146 ►Code: https://github.com/williamyang1991/StyleGANEX ►Project page: https://www.mmlab-ntu.com/project/styleganex/ ►Twitter: https://twitter.com/Whats_AI ►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/ ►Support me on Patreon: https://www.patreon.com/whatsai ►Join Our AI Discord: https://discord.gg/learnaitogether

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Keras provides a high-level API that simplifies the process of building and training complex neural network models. With a wide range of pre-built layers and functions, developers can easily build and train deep learning models on large datasets using optimization algorithms. Keras also supports GPU acceleration for training and inference, making it a popular choice for both research and industry applications. What are “Keras Datasets”? Keras datasets are preprocessed datasets that come pre-installed with the Keras library. These datasets are commonly used in the deep learning community for benchmarking models on various tasks such as image classification, text classification, and regression. By leveraging these datasets, developers can experiment with different deep learning models and easily compare their performance. This article looks at the , accessible to developers and researchers worldwide. Best Keras Datasets for Building and Training Deep Learning Models List of Keras Datasets 1. MNIST The MNIST dataset is popular and widely used in the fields of machine learning and computer vision. It consists of 70,000 grayscale images of handwritten digits 0–9, with 60,000 images for training and 10,000 for testing. Each image is 28x28 pixels in size and has a corresponding label denoting which digits it represents. This dataset can be downloaded from or loaded from : Kaggle Keras tf.keras.datasets.mnist.load_data(path="mnist.npz") 2. CIFAR-10 The CIFAR-10 dataset consists of 60,000 32x32 colour images in 10 classes, with 6,000 images per class. It has a total of 50,000 training images and 10,000 test images which is further divided into five training batches and one test batch, each with 10,000 images. This dataset can be downloaded from , or loaded from : Kaggle Keras tf.keras.datasets.cifar10.load_data() 3. CIFAR-100 The CIFAR-100 dataset has 60,000 (50,000 training images and 10,000 test images) 32x32 colour images in 100 classes, with 600 images per class. The 100 classes are grouped into 20 super-classes, with a fine label to denote its class and a coarse label to represent the super-class that it belongs to. This dataset can be downloaded from , or loaded from : Kaggle Keras tf.keras.datasets.cifar100.load_data(label_mode="fine") 4. Fashion-MNIST The Fashion MNIST dataset was created by Zalando Research as a replacement for the original MNIST dataset. The Fashion MNIST dataset consists of 70,000 grayscale images(training set of 60,000 and a test set of 10,000) of clothing items. The images are 28x28 pixels in size and represent 10 different classes of clothing items, including ­T-shirts/tops, trousers, pullovers, dresses, coats, sandals, shirts, sneakers, bags, and ankle boots. It is similar to the original MNIST dataset, but with more challenging classification tasks due to the greater complexity and variety of the clothing items. This dataset can be downloaded from , or loaded from : Kaggle Keras tf.keras.datasets.fashion_mnist.load_data() 5. IMDB The IMDB dataset is commonly used for sentiment analysis tasks, where the goal is to classify the reviews as positive or negative based on their content. It consists of a collection of 50,000 movie reviews (training set of 25,000 and a test set of 25,000) from the Internet Movie Database website, split evenly between positive and negative reviews. Each review in this dataset is a text document, preprocessed and transformed into sequences of integers, where each integer represents a word in the review. The vocabulary size is limited to the 10,000 most frequent words in the dataset and any less frequent words are replaced with a special “unknown” token. This dataset can be downloaded from , or loaded from : Kaggle Keras tf.keras.datasets.imdb.load_data(
    path="imdb.npz",
    num_words=None,
    skip_top=0,
    maxlen=None,
    seed=113,
    start_char=1,
    oov_char=2,
    index_from=3,
    **kwargs
) 6. Boston Housing The Boston Housing dataset contains information about housing in the Boston area. This information consists of 506 instances (404 training and 102 test instances), with attributes for each instance. The attributes have a mix of quantitative and categorical variables, such as the average number of rooms per dwelling, per capita crime rate and the proportion of non-retail business acres per town. This dataset can be downloaded from , or loaded from : Kaggle Keras tf.keras.datasets.boston_housing.load_data(
    path="boston_housing.npz", test_split=0.2, seed=113
) 7. Wine Quality The Wine Quality dataset contains information on red and white wine samples. The goal of this dataset is to classify the quality of the wine based on chemical properties like pH, density, alcohol content and citric acid content. The variables in this dataset include: Fixed Acidity - The number of fixed acids in the wine, expressed in g/dm^3. Volatile Acidity - The number of volatile acids in the wine, expressed in g/dm^3. Citric Acid - The amount of citric acid in the wine, expressed in g/dm^3. Residual Sugar: The amount of residual sugar in the wine, expressed in g/dm^3. Chlorides - The amount of chloride in the wine, expressed in g/dm^3. Free Sulfur Dioxide - The amount of free sulfur dioxide in the wine, expressed in mg/dm^3. Total Sulfur Dioxide - The amount of total sulfur dioxide in the wine, expressed in mg/dm^3. Density - The density of the wine, expressed in g/cm^3. pH - The pH level of the wine. Sulphates - The number of sulphates in the wine, expressed in g/dm^3. Alcohol - The alcohol content of the wine, expressed in % vol. Quality - The quality rating of the wine, on a scale of 0 to 10. You can download the dataset , or it can be loaded from Keras: here from keras.datasets import wine_quality

(X_train, y_train), (X_test, y_test) = wine_quality.load_data(test_split=0.2, seed=113) 8. Reuters Newswire The Reuters Newswire dataset is a preprocessed version of the original Reuters dataset, with the text encoded as sequences of integers. It consists of 11,228 news articles with a vocabulary of 30,979 words. Each article is classified into one of 46 different topics like “corn”, “crude”, “earnings” and “acquisitions”. You can download the dataset from , or it can be loaded from : Kaggle Keras tf.keras.datasets.reuters.load_data(path="reuters.npz",num_words=None,skip_top=0,
maxlen=None,test_split=0.2,seed=113,start_char=1,oov_char=2,index_from=3,**kwargs) 9. Pima Indians Diabetes This dataset consists of medical data about Pima Indian women, such as age, number of pregnancies, glucose levels, blood pressure, skin thickness, BMI and insulin level. The Keras version of the Pima Indians Diabetes dataset contains 768 samples with 8 input variables and 1 output variable. The Pima Indians Diabetes dataset can be downloaded on , or it can be loaded from Keras: Kaggle from tensorflow.keras.datasets import pima_indians_diabetes

(x_train, y_train), (x_test, y_test) = pima_indians_diabetes.load_data() 10. Dogs vs Cats The Dogs vs Cats dataset consists of 25,000 labelled images of dogs and cats, with 12,500 images of each class. These images were collected from various sources with varying sizes and quality. You can download the dataset from , or it can be loaded from Keras: Kaggle # Import the necessary Keras libraries:
from keras.preprocessing.image import ImageDataGenerator

# Set the paths to the training and validation directories:
train_dir = 'path/to/train'
validation_dir = 'path/to/validation'

# Define an ImageDataGenerator object to perform data augmentation and normalization:
train_datagen = ImageDataGenerator(rescale=1./255,
                                   rotation_range=40,
                                   width_shift_range=0.2,
                                   height_shift_range=0.2,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   horizontal_flip=True)

# Use flow_from_directory to load directory data in Keras:
validation_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(train_dir,
                                                    target_size=(150, 150),
                                                    batch_size=32,
                                                    class_mode='binary')

validation_generator = validation_datagen.flow_from_directory(validation_dir,
                                                              target_size=(150, 150),
                                                              batch_size=32,
                                                              class_mode='binary')

# The flow_from_directory yields preprocessed image batches and labels as DirectoryIterator. Note that in the above code, we are using data augmentation to create variations of the training images to help prevent overfitting. The validation data is not augmented. Common Use Cases for Keras Datasets - Handwritten digit recognition. MNIST - Object recognition in images. CIFAR-10 - Object recognition in images (more fine-grained than CIFAR-10). CIFAR-100 - Clothing item recognition. Fashion-MNIST - Sentiment analysis on movie reviews. IMDB - Regression on housing prices. Boston Housing - Classification of wine quality. Wine Quality - Topic classification of news articles. Reuters Newswire - Binary classification of diabetes in Pima Indian women. Pima Indians Diabetes - Binary classification of images of dogs and cats. Dogs vs Cats Final Thoughts Keras datasets are a valuable resource for machine learning practitioners and researchers, which can save time and effort in data collection and preprocessing, allowing for more focus on model development and experimentation. These Keras datasets are also available for anyone to download and use freely. More Dataset Listicles: Power Bi Datasets Time Series Datasets Geospatial Datasets Hugging Datasets R Datasets

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