18.1k citations · 37.8k across the 43 of their papers we have counts for
49 papers
A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning
Christoph Feichtenhofer, Haoqi Fan, Bo Xiong +2
We present a large-scale study on unsupervised spatiotemporal representation learning from videos. With a unified perspective on four recent image-based frameworks, we study a simp…
Exploring Simple Siamese Representation Learning
Xinlei Chen, Kaiming He
Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentat…
Graph Structure of Neural Networks
Jiaxuan You, Jure Leskovec, Kaiming He +1
Neural networks are often represented as graphs of connections between neurons. However, despite their wide use, there is currently little understanding of the relationship between…
Designing Network Design Spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick +2
In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settin…
Improved Baselines with Momentum Contrastive Learning
Xinlei Chen, Haoqi Fan, Ross Girshick +1
Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR'…
Are Labels Necessary for Neural Architecture Search?
Chenxi Liu, Piotr Dollár, Kaiming He +3
Existing neural network architectures in computer vision -- whether designed by humans or by machines -- were typically found using both images and their associated labels. In this…