Table of Links Abstract and 1 Introduction Abstract and 1 Introduction Related work
2.1. Generative Data Augmentation
2.2. Active Learning and Data Analysis


Preliminary


Our method
4.1. Estimation of Contribution in the Ideal Scenario
4.2. Batched Streaming Generative Active Learning


Experiments and 5.1. Offline Setting
5.2. Online Setting


Conclusion, Broader Impact, and References Related work
2.1. Generative Data Augmentation
2.2. Active Learning and Data Analysis Related work 2.1. Generative Data Augmentation 2.1. Generative Data Augmentation 2.2. Active Learning and Data Analysis 2.2. Active Learning and Data Analysis Preliminary Preliminary Preliminary Our method
4.1. Estimation of Contribution in the Ideal Scenario
4.2. Batched Streaming Generative Active Learning Our method 4.1. Estimation of Contribution in the Ideal Scenario 4.1. Estimation of Contribution in the Ideal Scenario 4.2. Batched Streaming Generative Active Learning 4.2. Batched Streaming Generative Active Learning Experiments and 5.1. Offline Setting
5.2. Online Setting Experiments and 5.1. Offline Setting Experiments and 5.1. Offline Setting 5.2. Online Setting 5.2. Online Setting Conclusion, Broader Impact, and References Conclusion, Broader Impact, and References Conclusion, Broader Impact, and References A. Implementation Details A. Implementation Details B. More ablations B. More ablations C. Discussion C. Discussion D. Visualization D. Visualization 2. Related work 2.1. Generative Data Augmentation Generative data augmentation (GDA) refers to using generative models to synthesize additional data for augmentation. With the continuous improvement in the capabilities of generative models (Goodfellow et al., 2020; Saharia et al., 2022; Rombach et al., 2022b), GDA has become a popular technique for improving model performance. Several works use GDA in perceptual tasks such as classification (Feng et al., 2023; Zhang et al., 2023; Azizi et al., 2023), detection (Zhao et al., 2023; Chen et al., 2023), and segmentation (Li et al., 2023b; Wu et al., 2023b;a; Xie et al., 2023). Early works (Zhang et al., 2021; Li et al., 2022) involve the use of generative models like Generative Adversarial Networks (GANs) (Goodfellow et al., 2020) to generate additional training data. With the evolution of diffusion models, recent works (Azizi et al., 2023; Li et al., 2023b; Wu et al., 2023b; Yang et al., 2023; Zhao et al., 2023) favor using high-quality diffusion models such as Imagen (Saharia et al., 2022) and Stable Diffusion (Rombach et al., 2022b) for data generation. X-Paste (Zhao et al., 2023) has proven the strategy of using copy-paste to be more effective than directly using generated data for mixed training, and for the first time demonstrated that using generated data can enhance the performance of segmentation models on the long-tailed segmentation dataset LVIS (Gupta et al., 2019). Therefore, we consider it as the baseline for our work. However, while previous works have investigated the effects of GDA on different tasks, there has been limited exploration on how to better filter and utilize generative data for downstream perception models. Authors:
(1) Muzhi Zhu, with equal contribution from Zhejiang University, China;
(2) Chengxiang Fan, 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) Weian Mao, Zhejiang University, China and The University of Adelaide, Australia;
(6) Xiaogang Xu, Zhejiang University, China;
(7) Chunhua Shen, Zhejiang University, China (chunhuashen@zju.edu.cn). Authors: Authors: (1) Muzhi Zhu, with equal contribution from Zhejiang University, China; (2) Chengxiang Fan, 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) Weian Mao, Zhejiang University, China and The University of Adelaide, Australia; (6) Xiaogang Xu, Zhejiang University, China; (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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From GANs to Diffusion: GDA for Perception Tasks

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