290 citations · 1.9k across the 59 of their papers we have counts for
8 papers · 1 filter
Multimodal Federated Learning via Contrastive Representation Ensemble
Qiying Yu, Yang Liu, Yimu Wang +2
With the increasing amount of multimedia data on modern mobile systems and IoT infrastructures, harnessing these rich multimodal data without breaching user privacy becomes a criti…
Adversarial Feature Augmentation and Normalization for Visual Recognition
Tianlong Chen, Yu Cheng, Zhe Gan +4
Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective…
Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective
Tianlong Chen, Yu Cheng, Zhe Gan +2
Training generative adversarial networks (GANs) with limited real image data generally results in deteriorated performance and collapsed models. To conquer this challenge, we are i…
Wasserstein Contrastive Representation Distillation
Liqun Chen, Dong Wang, Zhe Gan +3
The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a teacher network into a student network, with the latter being more compa…
Efficient Robust Training via Backward Smoothing
Jinghui Chen, Yu Cheng, Zhe Gan +2
Adversarial training is so far the most effective strategy in defending against adversarial examples. However, it suffers from high computational costs due to the iterative adversa…
MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of Gradients
Chen Zhu, Yu Cheng, Zhe Gan +3
Adaptive gradient methods such as RMSProp and Adam use exponential moving estimate of the squared gradient to compute adaptive step sizes, achieving better convergence than SGD in…
APo-VAE: Text Generation in Hyperbolic Space
Shuyang Dai, Zhe Gan, Yu Cheng +3
Natural language often exhibits inherent hierarchical structure ingrained with complex syntax and semantics. However, most state-of-the-art deep generative models learn embeddings…
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…