activity
20172023
most citedActivation Maximization Generative Adversarial Nets

47 citations · 102 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2023★ 2 cited

Camouflaged Object Detection with Feature Grafting and Distractor Aware

Yuxuan Song, Xinyue Li, Lin Qi

The task of Camouflaged Object Detection (COD) aims to accurately segment camouflaged objects that integrated into the environment, which is more challenging than ordinary detectio…

cs.LG2023

Towards High-Performance Exploratory Data Analysis (EDA) Via Stable Equilibrium Point

Yuxuan Song, Yongyu Wang

Exploratory data analysis (EDA) is a vital procedure for data science projects. In this work, we introduce a stable equilibrium point (SEP) - based framework for improving the effi…

cs.LG2023

Accelerate Support Vector Clustering via Spectrum-Preserving Data Compression

Yuxuan Song, Yongyu Wang

This paper proposes a novel framework for accelerating support vector clustering. The proposed method first computes much smaller compressed data sets while preserving the key clus…

cs.LG2020★ 3 cited

Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip

Yuxuan Song, Minkai Xu, Lantao Yu +3

Although Shannon theory states that it is asymptotically optimal to separate the source and channel coding as two independent processes, in many practical communication scenarios t…

cs.LG2019★ 10 cited

Improving Unsupervised Domain Adaptation with Variational Information Bottleneck

Yuxuan Song, Lantao Yu, Zhangjie Cao +5

Domain adaptation aims to leverage the supervision signal of source domain to obtain an accurate model for target domain, where the labels are not available. To leverage and adapt…

cs.LG2019★ 39 cited

Lipschitz Generative Adversarial Nets

Zhiming Zhou, Jiadong Liang, Yuxuan Song +5

In this paper, we study the convergence of generative adversarial networks (GANs) from the perspective of the informativeness of the gradient of the optimal discriminative function…

cs.CV2018★ 1 cited

Guiding the One-to-one Mapping in CycleGAN via Optimal Transport

Guansong Lu, Zhiming Zhou, Yuxuan Song +2

CycleGAN is capable of learning a one-to-one mapping between two data distributions without paired examples, achieving the task of unsupervised data translation. However, there is…

cs.LG2017★ 47 cited

Activation Maximization Generative Adversarial Nets

Zhiming Zhou, Han Cai, Shu Rong +5

Class labels have been empirically shown useful in improving the sample quality of generative adversarial nets (GANs). In this paper, we mathematically study the properties of the…