2.3k citations · 3.1k across the 21 of their papers we have counts for
8 papers · 1 filter
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…
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
Kihyuk Sohn, Jinsung Yoon, Chun-Liang Li +2
We study anomaly clustering, grouping data into coherent clusters of anomaly types. This is different from anomaly detection that aims to divide anomalies from normal data. Unlike…
Robust Contrastive Learning Using Negative Samples with Diminished Semantics
Songwei Ge, Shlok Mishra, Haohan Wang +2
Unsupervised learning has recently made exceptional progress because of the development of more effective contrastive learning methods. However, CNNs are prone to depend on low-lev…
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…
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…
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…
A Simple Semi-Supervised Learning Framework for Object Detection
Kihyuk Sohn, Zizhao Zhang, Chun-Liang Li +3
Semi-supervised learning (SSL) has a potential to improve the predictive performance of machine learning models using unlabeled data. Although there has been remarkable recent prog…
Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
Xi Ouyang, Yu Cheng, Yifan Jiang +2
State-of-the-art pedestrian detection models have achieved great success in many benchmarks. However, these models require lots of annotation information and the labeling process u…