435 citations · 607 across the 16 of their papers we have counts for
16 papers
A Survey on Extreme Multi-label Learning
Tong Wei, Zhen Mao, Jiang-Xin Shi +2
Multi-label learning has attracted significant attention from both academic and industry field in recent decades. Although existing multi-label learning algorithms achieved good pe…
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…
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…
Prototypical Classifier for Robust Class-Imbalanced Learning
Tong Wei, Jiang-Xin Shi, Yu-Feng Li +1
Deep neural networks have been shown to be very powerful methods for many supervised learning tasks. However, they can also easily overfit to training set biases, i.e., label noise…
Dash: Semi-Supervised Learning with Dynamic Thresholding
Yi Xu, Lei Shang, Jinxing Ye +5
While semi-supervised learning (SSL) has received tremendous attentions in many machine learning tasks due to its successful use of unlabeled data, existing SSL algorithms use eith…