activity
20172022
most citedLearning and Evaluating Representations for Deep One-class Classification

93 citations · 120 across the 8 of their papers we have counts for

collaborators

17 papers

cs.CV20222 cited

Hyperbolic Contrastive Learning for Visual Representations beyond Objects

Songwei Ge, Shlok Mishra, Simon Kornblith +2

Although self-/un-supervised methods have led to rapid progress in visual representation learning, these methods generally treat objects and scenes using the same lens. In this pap…

cs.LG20225 cited

Decoupling Local and Global Representations of Time Series

Sana Tonekaboni, Chun-Liang Li, Sercan Arik +2

Real-world time series data are often generated from several sources of variation. Learning representations that capture the factors contributing to this variability enables a bett…

cs.CV202116 cited

Object-aware Contrastive Learning for Debiased Scene Representation

Sangwoo Mo, Hyunwoo Kang, Kihyuk Sohn +2

Contrastive self-supervised learning has shown impressive results in learning visual representations from unlabeled images by enforcing invariance against different data augmentati…

cs.CL2021

ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction

Chen-Yu Lee, Chun-Liang Li, Chu Wang +5

Natural reading orders of words are crucial for information extraction from form-like documents. Despite recent advances in Graph Convolutional Networks (GCNs) on modeling spatial…

cs.CV202093 cited

Learning and Evaluating Representations for Deep One-class Classification

Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon +2

We present a two-stage framework for deep one-class classification. We first learn self-supervised representations from one-class data, and then build one-class classifiers on lear…

cs.CV2020

PseudoSeg: Designing Pseudo Labels for Semantic Segmentation

Yuliang Zou, Zizhao Zhang, Han Zhang +4

Recent advances in semi-supervised learning (SSL) demonstrate that a combination of consistency regularization and pseudo-labeling can effectively improve image classification accu…