Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning

20 May 2024

Papers citing "Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning"

9 / 9 papers shown

Title
Browsing without Third-Party Cookies: What Do You See? Maxwell Lin Shihan Lin Helen Wu Karen Wang Xiaowei Yang BDL 56 0 0 14 Oct 2024
Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning? Cheng Han Qifan Wang Yiming Cui Wenguan Wang Lifu Huang Siyuan Qi Dongfang Liu VLM 49 19 0 23 Jan 2024
Parameter-Efficient Tuning Makes a Good Classification Head Zhuoyi Yang Ming Ding Yanhui Guo Qingsong Lv Jie Tang VLM 40 14 0 30 Oct 2022
AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition Shoufa Chen Chongjian Ge Zhan Tong Jiangliu Wang Yibing Song Jue Wang Ping Luo 146 638 0 26 May 2022
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning Yidong Wang Hao Chen Qiang Heng Wenxin Hou Yue Fan ... Marios Savvides T. Shinozaki Bhiksha Raj Bernt Schiele Xing Xie 185 258 0 15 May 2022
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling Bowen Zhang Yidong Wang Wenxin Hou Hao Wu Jindong Wang Manabu Okumura T. Shinozaki AAML 252 862 0 15 Oct 2021
Learning to Prompt for Vision-Language Models Kaiyang Zhou Jingkang Yang Chen Change Loy Ziwei Liu VPVLM CLIP VLM 345 2,271 0 02 Sep 2021
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision Chao Jia Yinfei Yang Ye Xia Yi-Ting Chen Zarana Parekh Hieu H. Pham Quoc V. Le Yun-hsuan Sung Zhen Li Tom Duerig VLM CLIP 304 3,708 0 11 Feb 2021
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning Mamshad Nayeem Rizve Kevin Duarte Y. S. Rawat M. Shah 238 509 0 15 Jan 2021