970 citations · 3k across the 88 of their papers we have counts for
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Robust Contrastive Learning against Noisy Views
Ching-Yao Chuang, R Devon Hjelm, Xin Wang +5
Contrastive learning relies on an assumption that positive pairs contain related views, e.g., patches of an image or co-occurring multimodal signals of a video, that share certain…
Deep Metric Learning via Facility Location
Hyun Oh Song, Stefanie Jegelka, Vivek Rathod +1
Learning the representation and the similarity metric in an end-to-end fashion with deep networks have demonstrated outstanding results for clustering and retrieval. However, these…
Deep Metric Learning via Lifted Structured Feature Embedding
Hyun Oh Song, Yu Xiang, Stefanie Jegelka +1
Learning the distance metric between pairs of examples is of great importance for learning and visual recognition. With the remarkable success from the state of the art convolution…
Auxiliary Image Regularization for Deep CNNs with Noisy Labels
Samaneh Azadi, Jiashi Feng, Stefanie Jegelka +1
Precisely-labeled data sets with sufficient amount of samples are very important for training deep convolutional neural networks (CNNs). However, many of the available real-world d…
Convex Optimization for Parallel Energy Minimization
K. S. Sesh Kumar, Alvaro Barbero, Stefanie Jegelka +2
Energy minimization has been an intensely studied core problem in computer vision. With growing image sizes (2D and 3D), it is now highly desirable to run energy minimization algor…
Weakly-supervised Discovery of Visual Pattern Configurations
Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka +1
The increasing prominence of weakly labeled data nurtures a growing demand for object detection methods that can cope with minimal supervision. We propose an approach that automati…
On learning to localize objects with minimal supervision
Hyun Oh Song, Ross Girshick, Stefanie Jegelka +3
Learning to localize objects with minimal supervision is an important problem in computer vision, since large fully annotated datasets are extremely costly to obtain. In this paper…