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 B. More ablations Momentum Coefficient β. In Algorithm 3, we introduce a momentum coefficient β to update the grad cache. Here we explore the effect of different β on the model performance. A larger beta signifies a greater focus on global information, while a smaller beta indicates a higher attention to the current test batch Ub. Detailed results are presented in Table 9. Observations suggest that when β is 0.1, the performance is the best, which is also the β we finally adopted. Momentum Coefficient Contribution threshold τ. In Algorithm 3, we incorporate a contribution threshold τ , intended for filtering the produced data. Here we investigate the impact of varying values of τ on the model’s performance. The larger τ implies a stricter filtration of the generated data, while the smaller τ signifies a looser filtering of the generated data. The specific results are shown in Table 10. We can see the performance is optimal when τ equals -0.05, which is also the τ we eventually settle on for our final model. Contribution threshold Online learning vs. Offline learning We compare online learning and offline learning under different iterations. The result is shown in Figure 9. Online learning vs. Offline learning 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

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Online vs. Offline Active Learning: Performance Comparison Across Iterations

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

3D Semantic Instance Maps: Leveraging Foundation Models for Language-Guided Navigation

It Is Okay If You Don't Know What You Like. We Do (feat. Deep Recommendation Algorithms)

10 Machine Learning, Data Science, and Deep Learning Courses for Programmers in 2020

10 Computer Vision Startups on Product Hunt with the Most Upvotes

10 Best Entry Level Machine Learning Tutorials

10 Best + Free Machine Learning Courses Collection

3D Semantic Instance Maps: Leveraging Foundation Models for Language-Guided Navigation

It Is Okay If You Don't Know What You Like. We Do (feat. Deep Recommendation Algorithms)

10 Machine Learning, Data Science, and Deep Learning Courses for Programmers in 2020

10 Computer Vision Startups on Product Hunt with the Most Upvotes

10 Best Entry Level Machine Learning Tutorials

10 Best + Free Machine Learning Courses Collection

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps