153 citations · 165 across the 4 of their papers we have counts for
6 papers · 1 filter
A simple, efficient and scalable contrastive masked autoencoder for learning visual representations
Shlok Mishra, Joshua Robinson, Huiwen Chang +4
We introduce CAN, a simple, efficient and scalable method for self-supervised learning of visual representations. Our framework is a minimal and conceptually clean synthesis of (C)…
Unsupervised Disentanglement without Autoencoding: Pitfalls and Future Directions
Andrea Burns, Aaron Sarna, Dilip Krishnan +1
Disentangled visual representations have largely been studied with generative models such as Variational AutoEncoders (VAEs). While prior work has focused on generative methods for…
What Makes for Good Views for Contrastive Learning?
Yonglong Tian, Chen Sun, Ben Poole +3
Contrastive learning between multiple views of the data has recently achieved state of the art performance in the field of self-supervised representation learning. Despite its succ…
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?
Yonglong Tian, Yue Wang, Dilip Krishnan +2
The focus of recent meta-learning research has been on the development of learning algorithms that can quickly adapt to test time tasks with limited data and low computational cost…
Boundless: Generative Adversarial Networks for Image Extension
Piotr Teterwak, Aaron Sarna, Dilip Krishnan +4
Image extension models have broad applications in image editing, computational photography and computer graphics. While image inpainting has been extensively studied in the literat…
Contrastive Multiview Coding
Yonglong Tian, Dilip Krishnan, Phillip Isola
Humans view the world through many sensory channels, e.g., the long-wavelength light channel, viewed by the left eye, or the high-frequency vibrations channel, heard by the right e…