activity
20162022
most citedFeature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection

175 citations · 268 across the 18 of their papers we have counts for

collaborators

37 papers

cs.LG20222 cited

Tucker-O-Minus Decomposition for Multi-view Tensor Subspace Clustering

Yingcong Lu, Yipeng Liu, Zhen Long +2

With powerful ability to exploit latent structure of self-representation information, different tensor decompositions have been employed into low rank multi-view clustering (LRMVC)…

cs.LG20211 cited

Semi-tensor Product-based TensorDecomposition for Neural Network Compression

Hengling Zhao, Yipeng Liu, Xiaolin Huang +1

The existing tensor networks adopt conventional matrix product for connection. The classical matrix product requires strict dimensionality consistency between factors, which can re…

cs.LG2021175 cited

Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection

Yingjie Zhou, Xucheng Song, Yanru Zhang +3

Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data. Recent works build deep neural networks…

cs.LG20215 cited

Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions

Jing Wu, Mingyi Zhou, Ce Zhu +3

Recently, adversarial attack methods have been developed to challenge the robustness of machine learning models. However, mainstream evaluation criteria experience limitations, eve…

eess.IV202122 cited

Real-World Single Image Super-Resolution: A Brief Review

Honggang Chen, Xiaohai He, Linbo Qing +3

Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) observation, has been an active research topic in the area o…

cs.CV20212 cited

Scalable Deep Compressive Sensing

Zhonghao Zhang, Yipeng Liu, Xingyu Cao +2

Deep learning has been used to image compressive sensing (CS) for enhanced reconstruction performance. However, most existing deep learning methods train different models for diffe…