On the Marginal Benefit of Active Learning: Does Self-Supervision Eat
Its Cake?

On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake?

16 November 2020

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Papers citing "On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake?"

17 / 17 papers shown

Title
Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection Qian Shao Jiangrui Kang Qiyuan Chen Zepeng Li Hongxia Xu Yiwen Cao Jiajuan Liang Jian Wu 87 1 0 18 Sep 2024
Making Better Use of Unlabelled Data in Bayesian Active Learning Freddie Bickford-Smith Adam Foster Tom Rainforth 148 6 0 26 Apr 2024
Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained Models Ziting Wen Oscar Pizarro Stefan B. Williams 91 0 0 02 Mar 2024
Revisiting Active Learning in the Era of Vision Foundation Models S. Gupte Josiah Aklilu Jeffrey Nirschl Serena Yeung-Levy VLM 105 6 0 25 Jan 2024
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning Sandra Gilhuber Rasmus Hvingelby Mang Ling Ada Fok Thomas Seidl 94 1 0 16 Aug 2023
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning Jifan Zhang Yifang Chen Gregory H. Canal Stephen Mussmann Arnav M. Das ... Yinglun Zhu Jeffrey Bilmes S. Du Kevin Jamieson Robert D. Nowak VLM 155 19 0 16 Jun 2023
NTKCPL: Active Learning on Top of Self-Supervised Model by Estimating True Coverage Ziting Wen Oscar Pizarro Stefan B. Williams 140 2 0 07 Jun 2023
How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget Guy Hacohen D. Weinshall 135 12 0 06 Jun 2023
Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning Evelyn J. Mannix H. Bondell SSL 91 0 0 17 May 2023
Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment Carsten T. Lüth Till J. Bungert Lukas Klein Paul F. Jaeger 156 15 0 25 Jan 2023
An Empirical Study on the Efficacy of Deep Active Learning for Image Classification Yu Li Mu-Hwa Chen Yannan Liu Daojing He Qiang Xu 124 9 0 30 Nov 2022
Deep Active Learning for Computer Vision: Past and Future Rinyoichi Takezoe Xu Liu Shunan Mao Marco Tianyu Chen Zhanpeng Feng Shiliang Zhang Xiaoyu Wang VLM 114 23 0 27 Nov 2022
Active Learning Through a Covering Lens Ofer Yehuda Avihu Dekel Guy Hacohen D. Weinshall 155 59 0 23 May 2022
A Comparative Survey of Deep Active Learning Xueying Zhan Qingzhong Wang Kuan-Hao Huang Haoyi Xiong Dejing Dou Antoni B. Chan FedML HAI 200 116 0 25 Mar 2022
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets Guy Hacohen Avihu Dekel D. Weinshall 311 136 0 06 Feb 2022
Active Learning at the ImageNet Scale Z. Emam Hong-Min Chu Ping Yeh-Chiang W. Czaja R. Leapman Micah Goldblum Tom Goldstein 116 36 0 25 Nov 2021
Unsupervised Selective Labeling for More Effective Semi-Supervised Learning Xudong Wang Long Lian Stella X. Yu 421 35 0 06 Oct 2021