Web: https://www.reddit.com/r/MachineLearning/comments/sgv1xi/d_representation_learning_selfsupervision_methods/

Jan. 31, 2022, 7:05 a.m. | /u/Cute-Ad77

Machine Learning reddit.com

I understand that a contrastive learning approach such as SimCLR has an inherent problem when dealing with a low number of classes (let's say 2,3,5,6, maybe even 10). Problem is that the chances of picking a negative sample that has the same label as the image from the positive pair is not low (let's say a dog and another dog)

Which contrastive learning approaches do better on such problems that we have let's say 4 classes rather than 1000 (or …

learning machinelearning representation representation learning

Data Engineer, Buy with Prime

@ Amazon.com | Santa Monica, California, USA

Data Architect – Public Sector Health Data Architect, WWPS

@ Amazon.com | US, VA, Virtual Location - Virginia

[Job 8224] Data Engineer - Developer Senior

@ CI&T | Brazil

Software Engineer, Machine Learning, Planner/Behavior Prediction

@ Nuro, Inc. | Mountain View, California (HQ)

Lead Data Scientist

@ Inspectorio | Ho Chi Minh City, Ho Chi Minh City, Vietnam - Remote

Data Engineer

@ Craftable | Portugal - Remote