42 citations · 91 across the 20 of their papers we have counts for
10 papers · 1 filter
Unlabeled Data vs. Pre-trained Knowledge: Rethinking SSL in the Era of Large Models
Song-Lin Lv, Rui Zhu, Tong Wei +2
Semi-supervised learning (SSL) alleviates the cost of data labeling process by exploiting unlabeled data and has achieved promising results. Meanwhile, with the development of larg…
Robust Semi-Supervised Learning in Open Environments
Lan-Zhe Guo, Lin-Han Jia, Jie-Jing Shao +1
Semi-supervised learning (SSL) aims to improve performance by exploiting unlabeled data when labels are scarce. Conventional SSL studies typically assume close environments where i…
Fully Test-time Adaptation for Tabular Data
Zhi Zhou, Kun-Yang Yu, Lan-Zhe Guo +1
Tabular data plays a vital role in various real-world scenarios and finds extensive applications. Although recent deep tabular models have shown remarkable success, they still stru…
Offline Imitation Learning with Model-based Reverse Augmentation
Jie-Jing Shao, Hao-Sen Shi, Lan-Zhe Guo +1
In offline Imitation Learning (IL), one of the main challenges is the \textit{covariate shift} between the expert observations and the actual distribution encountered by the agent,…
USB: A Unified Semi-supervised Learning Benchmark for Classification
Yidong Wang, Hao Chen, Yue Fan +19
Semi-supervised learning (SSL) improves model generalization by leveraging massive unlabeled data to augment limited labeled samples. However, currently, popular SSL evaluation pro…
LAMDA-SSL: Semi-Supervised Learning in Python
Lin-Han Jia, Lan-Zhe Guo, Zhi Zhou +1
LAMDA-SSL is open-sourced on GitHub and its detailed usage documentation is available at https://ygzwqzd.github.io/LAMDA-SSL/. This documentation introduces LAMDA-SSL in detail fro…
Transfer and Share: Semi-Supervised Learning from Long-Tailed Data
Tong Wei, Qian-Yu Liu, Jiang-Xin Shi +2
Long-Tailed Semi-Supervised Learning (LTSSL) aims to learn from class-imbalanced data where only a few samples are annotated. Existing solutions typically require substantial cost…
Robust Deep Semi-Supervised Learning: A Brief Introduction
Lan-Zhe Guo, Zhi Zhou, Yu-Feng Li
Semi-supervised learning (SSL) is the branch of machine learning that aims to improve learning performance by leveraging unlabeled data when labels are insufficient. Recently, SSL…
Weakly Supervised Learning Meets Ride-Sharing User Experience Enhancement
Lan-Zhe Guo, Feng Kuang, Zhang-Xun Liu +3
Weakly supervised learning aims at coping with scarce labeled data. Previous weakly supervised studies typically assume that there is only one kind of weak supervision in data. In…
Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness
Lan-Zhe Guo, Yu-Feng Li, Ming Li +3
Weakly supervised data are widespread and have attracted much attention. However, since label quality is often difficult to guarantee, sometimes the use of weakly supervised data w…