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 3. Preliminary As shown in Algorithm 1, it displays the overall process of our baseline, which does not consider the different impacts each sample could impose on the model. In other words, our aim is to identify a function, ϕ(g, θ), capable of gauging the contribution of any given generated sample g ∈ G to the current model f. Then, via this scoring mechanism, we can filter and retain the most helpful samples for the model and simultaneously discard those that are useless or even harmful to the model. 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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Formalizing Generative Active Learning for Instance Segmentation

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