248 citations · 755 across the 30 of their papers we have counts for
8 papers · 1 filter
Revisiting Sparse Convolutional Model for Visual Recognition
Xili Dai, Mingyang Li, Pengyuan Zhai +6
Despite strong empirical performance for image classification, deep neural networks are often regarded as ``black boxes'' and they are difficult to interpret. On the other hand, sp…
Learning a Self-Expressive Network for Subspace Clustering
Shangzhi Zhang, Chong You, René Vidal +1
State-of-the-art subspace clustering methods are based on self-expressive model, which represents each data point as a linear combination of other data points. However, such method…
Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training
Sheng Liu, Xiao Li, Yuexiang Zhai +4
Normalization techniques have become a basic component in modern convolutional neural networks (ConvNets). In particular, many recent works demonstrate that promoting the orthogona…
Deep Isometric Learning for Visual Recognition
Haozhi Qi, Chong You, Xiaolong Wang +2
Initialization, normalization, and skip connections are believed to be three indispensable techniques for training very deep convolutional neural networks and obtaining state-of-th…
Self-Supervised Convolutional Subspace Clustering Network
Junjian Zhang, Chun-Guang Li, Chong You +4
Subspace clustering methods based on data self-expression have become very popular for learning from data that lie in a union of low-dimensional linear subspaces. However, the appl…
Provable Self-Representation Based Outlier Detection in a Union of Subspaces
Chong You, Daniel P. Robinson, René Vidal
Many computer vision tasks involve processing large amounts of data contaminated by outliers, which need to be detected and rejected. While outlier detection methods based on robus…
Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework
Chun-Guang Li, Chong You, René Vidal
Subspace clustering refers to the problem of segmenting data drawn from a union of subspaces. State-of-the-art approaches for solving this problem follow a two-stage approach. In t…
Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit
Chong You, Daniel P. Robinson, Rene Vidal
Subspace clustering methods based on , or nuclear norm regularization have become very popular due to their simplicity, theoretical guarantees and empirical succes…