Categories: FAANG

Poly-View Contrastive Learning

Contrastive learning typically matches pairs of related views among a number of unrelated negative views.
Views can be generated (e.g. by augmentations) or be observed. We investigate matching when there are more than two related views which we call poly-view tasks, and derive new representation learning objectives using information maximization and sufficient statistics.
We show that with unlimited computation, one should maximize the number of related views, and with a fixed compute budget, it is beneficial to decrease the number of unique samples whilst increasing the number of views of…

SimPer: Simple self-supervised learning of periodic targets

July 19, 2023

In "FAANG"

Multi-layered Mapping of Brain Tissue via Segmentation Guided Contrastive Learning

November 10, 2022

In "FAANG"

Contrasting Multiple Representations with the Multi-Marginal Matching Gap

Learning meaningful representations of complex objects that can be seen through multiple (k≥3kgeq 3k≥3) views or modalities is a core task in machine learning. Existing methods use losses originally intended for paired views, and extend them to kkk views, either by instantiating 12k(k−1)tfrac12k(k-1)21k(k−1) loss-pairs, or by using reduced embeddings, following…

July 16, 2024

In "FAANG"

AI Generated Robotic Content