224 citations · 3.2k across the 170 of their papers we have counts for
14 papers · 1 filter
Composite Binary Decomposition Networks
You Qiaoben, Zheng Wang, Jianguo Li +3
Binary neural networks have great resource and computing efficiency, while suffer from long training procedure and non-negligible accuracy drops, when comparing to the full-precisi…
Transferable Adversarial Attacks for Image and Video Object Detection
Xingxing Wei, Siyuan Liang, Ning Chen +1
Adversarial examples have been demonstrated to threaten many computer vision tasks including object detection. However, the existing attacking methods for object detection have two…
Semi-crowdsourced Clustering with Deep Generative Models
Yucen Luo, Tian Tian, Jiaxin Shi +2
We consider the semi-supervised clustering problem where crowdsourcing provides noisy information about the pairwise comparisons on a small subset of data, i.e., whether a sample p…
Lazy-CFR: fast and near optimal regret minimization for extensive games with imperfect information
Yichi Zhou, Tongzheng Ren, Jialian Li +2
Counterfactual regret minimization (CFR) is the most popular algorithm on solving two-player zero-sum extensive games with imperfect information and achieves state-of-the-art perfo…
Deep Structured Generative Models
Kun Xu, Haoyu Liang, Jun Zhu +2
Deep generative models have shown promising results in generating realistic images, but it is still non-trivial to generate images with complicated structures. The main reason is t…
Learning Implicit Generative Models by Teaching Explicit Ones
Chao Du, Kun Xu, Chongxuan Li +2
Implicit generative models are difficult to train as no explicit density functions are defined. Generative adversarial nets (GANs) present a minimax framework to train such models,…