Table of Links Abstract and 1 Introduction Abstract and 1 Introduction Related Work


Our Proposed DiverGen
3.1. Analysis of Data Distribution
3.2. Generative Data Diversity Enhancement
3.3. Generative Pipeline


Experiments
4.1. Settings
4.2. Main Results
4.3. Ablation Studies


Conclusions, Acknowledgments, and References Related Work Related Work Related Work Our Proposed DiverGen
3.1. Analysis of Data Distribution
3.2. Generative Data Diversity Enhancement
3.3. Generative Pipeline Our Proposed DiverGen 3.1. Analysis of Data Distribution 3.1. Analysis of Data Distribution 3.2. Generative Data Diversity Enhancement 3.2. Generative Data Diversity Enhancement 3.3. Generative Pipeline 3.3. Generative Pipeline Experiments
4.1. Settings
4.2. Main Results
4.3. Ablation Studies Experiments 4.1. Settings 4.1. Settings 4.2. Main Results 4.2. Main Results 4.3. Ablation Studies 4.3. Ablation Studies Conclusions, Acknowledgments, and References Conclusions, Acknowledgments, and References Conclusions, Acknowledgments, and References Appendix Appendix A. Implementation Details A. Implementation Details B. Visualization B. Visualization 4.3. Ablation Studies We analyze the effects of the proposed strategies in DiverGen through a series of ablation studies using the Swin-L [16] backbone. Effect of category diversity. We select 50, 250, and 566 extra categories from ImagNet-1K [23], and generate 0.5k images for each category, which are added to the baseline. The baseline only uses 1,203 categories of LIVS [8] to generate data. We show the results in Table 5. Generally, increasing the number of extra categories initially improves then declines model performance, peaking at 250 extra categories. The trend suggests that using extra categories to enhance category diversity can improve the model’s generalization capabilities, but too many extra categories may mislead the model, leading to a decrease in performance. Effect of category diversity Effect of prompt diversity. We select a subset of categories and use ChatGPT to generate 32 and 128 prompts for each category, with each prompt being used to generate 8 and 2 images, respectively, ensuring that the image count for each category is 0.25k. The baseline uses only one prompt per category to generate 0.25k images. The regenerated images will replace the corresponding categories in the baseline to ensure that the final data scale is consistent. The results are presented in Table 6. With the increase in prompt diversity, there is a continuous improvement in model performance, indicating that prompt diversity is indeed beneficial for enhancing model performance. Effect of prompt diversity. Effect of generative model diversity. We choose two commonly used generative models, Stable Diffusion [22] (SD) and DeepFloyd-IF [24] (IF). We generate 1k images per category for each generative model, totaling 1,200k. When using a mixed dataset (SD + IF), we take 600k from SD and 600k from IF per category, respectively, to ensure the total dataset scale is consistent. The baseline does not use any generative data (none). As shown in Table 7, using data generated by either SD or IF alone can improve performance, further mixing the generative data of both leads to significant performance gains. This demonstrates that increasing model diversity is beneficial for improving model performance. Effect of generative model diversity. Effect of annotation strategy. X-Paste [34] uses four models (U2Net [20], SelfReformer [31], UFO [25] and CLIPseg [17]) to generate masks and selects the one with the highest CLIP score. We compare our proposed annotation strategy (SAM-bg) to that proposed by X-Paste (max CLIP). In Table 8, SAM-bg outperforms max CLIP strategy across all metrics, indicating that our proposed strategy can produce better annotations, improving model performance. As shown in Figure 5, SAM-bg unlocks the potential capability of SAM, obtaining precise and refined masks. Effect of annotation strategy. Effect of CLIP inter-similarity. We compare our proposed CLIP inter-similarity to CLIP score [34]. The results are shown in Table 9. The performance of data filtered by CLIP inter-similarity is higher than that of CLIP score, demonstrating that CLIP inter-similarity can filter low-quality images more effectively Effect of CLIP inter-similarity. 5. Conclusions In this paper, we explain the role of generative data augmentation from the perspective of data distribution discrepancies and find that generative data can expand the data distribution that the model can learn, mitigating overfitting the training set. Furthermore, we find that data diversity of generative data is crucial for improving model performance. Therefore, we design an efficient data diversity enhancement strategy, Generative Data Diversity Enhancement. We design various diversity enhancement strategies to increase data diversity from the aspects of category diversity, prompt diversity, and generative model diversity. Finally, we optimize the data generative pipeline by designing the annotation strategy SAM-background to obtain higher quality annotations and introducing the metric CLIP inter-similarity to filter data, which further improves the quality of the generative dataset. Through these designed strategies, our proposed method significantly outperforms the existing strong models. We hope DiverGen can provide new insights and inspirations for future research on the effectiveness and efficiency of generative data augmentation. 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Learn., 2023. 1, 2, 3, 4, 5, 6, 7, 8, 12, 13 [35] Xingyi Zhou, Vladlen Koltun, and Philipp Krahenbuhl. Probabilistic two-stage detection. arXiv: Comp. Res. Repository, 2021. 6, 7 [36] Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krahenbuhl, and Ishan Misra. Detecting twenty-thousand ¨ classes using image-level supervision. In Proc. Eur. Conf. Comp. Vis., pages 350–368. Springer, 2022. 7 Authors:
(1) Chengxiang Fan, with equal contribution from Zhejiang University, China;
(2) Muzhi Zhu, with equal contribution from Zhejiang University, China;
(3) Hao Chen, Zhejiang University, China (haochen.cad@zju.edu.cn);
(4) Yang Liu, Zhejiang University, China;
(5) Weijia Wu, Zhejiang University, China;
(6) Huaqi Zhang, vivo Mobile Communication Co..
(7) Chunhua Shen, Zhejiang University, China (chunhuashen@zju.edu.cn). Authors: Authors: (1) Chengxiang Fan, with equal contribution from Zhejiang University, China; (2) Muzhi Zhu, with equal contribution from Zhejiang University, China; (3) Hao Chen, Zhejiang University, China (haochen.cad@zju.edu.cn); (4) Yang Liu, Zhejiang University, China; (5) Weijia Wu, Zhejiang University, China; (6) Huaqi Zhang, vivo Mobile Communication Co.. (7) Chunhua Shen, Zhejiang University, China (chunhuashen@zju.edu.cn). This paper is available on arxiv under CC BY-NC-ND 4.0 Deed (Attribution-Noncommercial-Noderivs 4.0 International) license. This paper is available on arxiv under CC BY-NC-ND 4.0 Deed (Attribution-Noncommercial-Noderivs 4.0 International) license. available on arxiv available on arxiv

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The Impact of Generative Data Diversity on AI Accuracy

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