activity
20172023
most citedALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

75 citations · 374 across the 37 of their papers we have counts for

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8 papers · 1 filter

cs.CV20227 cited

Lafite2: Few-shot Text-to-Image Generation

Yufan Zhou, Chunyuan Li, Changyou Chen +2

Text-to-image generation models have progressed considerably in recent years, which can now generate impressive realistic images from arbitrary text. Most of such models are traine…

cs.CV20217 cited

Unsupervised Hashing with Contrastive Information Bottleneck

Zexuan Qiu, Qinliang Su, Zijing Ou +2

Many unsupervised hashing methods are implicitly established on the idea of reconstructing the input data, which basically encourages the hashing codes to retain as much informatio…

cs.CV2021

Towards Fair Federated Learning with Zero-Shot Data Augmentation

Weituo Hao, Mostafa El-Khamy, Jungwon Lee +4

Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models while having no access to the…

cs.CV20203 cited

ReMP: Rectified Metric Propagation for Few-Shot Learning

Yang Zhao, Chunyuan Li, Ping Yu +1

Few-shot learning features the capability of generalizing from a few examples. In this paper, we first identify that a discriminative feature space, namely a rectified metric space…

cs.CV2020

Unpaired Image-to-Image Translation via Latent Energy Transport

Yang Zhao, Changyou Chen

Image-to-image translation aims to preserve source contents while translating to discriminative target styles between two visual domains. Most works apply adversarial learning in t…

cs.CV202014 cited

Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition

Tianhang Zheng, Sheng Liu, Changyou Chen +3

Skeleton-based action recognition has attracted increasing attention due to its strong adaptability to dynamic circumstances and potential for broad applications such as autonomous…