Contrastive Learning has recently received interest due to its success in self-supervised
representation learning in the computer vision domain. However, the origins of Contrastive …

In the last decade, deep supervised learning has had tremendous success. However, its
flaws, such as its dependency on manual and costly annotations on large datasets and …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

Mainstream 3D representation learning approaches are built upon contrastive or generative
modeling pretext tasks, where great improvements in performance on various downstream …

We present a novel masked image modeling (MIM) approach, context autoencoder (CAE),
for self-supervised representation pretraining. We pretrain an encoder by making predictions …

In this paper, we question if self-supervised learning provides new properties to Vision
Transformer (ViT) that stand out compared to convolutional networks (convnets). Beyond the …

Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large
computer vision benchmarks. A successful approach to SSL is to learn embeddings which …

Contrastive learning (CL) is one of the most successful paradigms for self-supervised
learning (SSL). In a principled way, it considers two augmented “views” of the same image …

Abstract Self-Supervised Learning (SSL) surmises that inputs and pairwise positive
relationships are enough to learn meaningful representations. Although SSL has recently …

We introduce a conceptually simple yet effective model for self-supervised representation
learning with graph data. It follows the previous methods that generate two views of an input …