41 citations · 58 across the 8 of their papers we have counts for
9 papers
Learning Where to Learn in Cross-View Self-Supervised Learning
Lang Huang, Shan You, Mingkai Zheng +3
Self-supervised learning (SSL) has made enormous progress and largely narrowed the gap with the supervised ones, where the representation learning is mainly guided by a projection…
Searching for Network Width with Bilaterally Coupled Network
Xiu Su, Shan You, Jiyang Xie +4
Searching for a more compact network width recently serves as an effective way of channel pruning for the deployment of convolutional neural networks (CNNs) under hardware constrai…
DyRep: Bootstrapping Training with Dynamic Re-parameterization
Tao Huang, Shan You, Bohan Zhang +4
Structural re-parameterization (Rep) methods achieve noticeable improvements on simple VGG-style networks. Despite the prevalence, current Rep methods simply re-parameterize all op…
SimMatch: Semi-supervised Learning with Similarity Matching
Mingkai Zheng, Shan You, Lang Huang +3
Learning with few labeled data has been a longstanding problem in the computer vision and machine learning research community. In this paper, we introduced a new semi-supervised le…
Relational Surrogate Loss Learning
Tao Huang, Zekang Li, Hua Lu +6
Evaluation metrics in machine learning are often hardly taken as loss functions, as they could be non-differentiable and non-decomposable, e.g., average precision and F1 score. Thi…
Weakly Supervised Contrastive Learning
Mingkai Zheng, Fei Wang, Shan You +4
Unsupervised visual representation learning has gained much attention from the computer vision community because of the recent achievement of contrastive learning. Most of the exis…