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"

CatLIP: CLIP-level Visual Recognition Accuracy with 2.7× Faster Pre-training on Web-scale Image-Text Data

Contrastive learning has emerged as a transformative method for learning effective visual representations through the alignment of image and text embeddings. However, pairwise similarity computation in contrastive loss between image and text pairs poses computational challenges. This paper presents a novel weakly supervised pre-training of vision models on web-scale image-text…

April 26, 2024

In "FAANG"

AI Generated Robotic Content