40 citations · 278 across the 23 of their papers we have counts for
10 papers · 1 filter
The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy
Tianlong Chen, Zhenyu Zhang, Yu Cheng +2
Vision transformers (ViTs) have gained increasing popularity as they are commonly believed to own higher modeling capacity and representation flexibility, than traditional convolut…
Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation
Shuyang Dai, Yu Cheng, Yizhe Zhang +3
Recent unsupervised approaches to domain adaptation primarily focus on minimizing the gap between the source and the target domains through refining the feature generator, in order…
A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning
Duo Wang, Yu Cheng, Mo Yu +2
Few-shot learning aims to learn classifiers for new classes with only a few training examples per class. Most existing few-shot learning approaches belong to either metric-based me…
Few-shot Learning with Meta Metric Learners
Yu Cheng, Mo Yu, Xiaoxiao Guo +1
Few-shot Learning aims to learn classifiers for new classes with only a few training examples per class. Existing meta-learning or metric-learning based few-shot learning approache…
Bayesian Cycle-Consistent Generative Adversarial Networks via Marginalizing Latent Sampling
Haoran You, Yu Cheng, Tianheng Cheng +2
Recent techniques built on Generative Adversarial Networks (GANs), such as Cycle-Consistent GANs, are able to learn mappings among different domains built from unpaired datasets, t…
Sobolev GAN
Youssef Mroueh, Chun-Liang Li, Tom Sercu +2
We propose a new Integral Probability Metric (IPM) between distributions: the Sobolev IPM. The Sobolev IPM compares the mean discrepancy of two distributions for functions (critic)…