3 papers
cs.CV2023
PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning
Florian Bordes, Shashank Shekhar, Mark Ibrahim +3
Synthetic image datasets offer unmatched advantages for designing and evaluating deep neural networks: they make it possible to (i) render as many data samples as needed, (ii) prec…
cs.CV2023
Stochastic positional embeddings improve masked image modeling
Amir Bar, Florian Bordes, Assaf Shocher +6
Masked Image Modeling (MIM) is a promising self-supervised learning approach that enables learning from unlabeled images. Despite its recent success, learning good representations…
cs.CV2023
Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning
Casey Meehan, Florian Bordes, Pascal Vincent +2
Self-supervised learning (SSL) algorithms can produce useful image representations by learning to associate different parts of natural images with one another. However, when taken…