1 citations · 1 across the 2 of their papers we have counts for
3 papers
Joint Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self Supervised Learning
Hugues Van Assel, Mark Ibrahim, Tommaso Biancalani +2
Reconstruction and joint embedding have emerged as two leading paradigms in Self Supervised Learning (SSL). Reconstruction methods focus on recovering the original sample from a di…
-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs
Vlad Sobal, Mark Ibrahim, Randall Balestriero +5
Learning good representations involves capturing the diverse ways in which data samples relate. Contrastive loss - an objective matching related samples - underlies methods from se…
Occam's Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations?
Mark Ibrahim, David Klindt, Randall Balestriero
Deep Learning is often depicted as a trio of data-architecture-loss. Yet, recent Self Supervised Learning (SSL) solutions have introduced numerous additional design choices, e.g.,…