89 citations · 97 across the 3 of their papers we have counts for
3 papers
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Patrick Esser, Sumith Kulal, Andreas Blattmann +14
Diffusion models create data from noise by inverting the forward paths of data towards noise and have emerged as a powerful generative modeling technique for high-dimensional, perc…
The Role of Pre-training Data in Transfer Learning
Rahim Entezari, Mitchell Wortsman, Olga Saukh +3
The transfer learning paradigm of model pre-training and subsequent fine-tuning produces high-accuracy models. While most studies recommend scaling the pre-training size to benefit…
Studying the impact of magnitude pruning on contrastive learning methods
Francesco Corti, Rahim Entezari, Sara Hooker +2
We study the impact of different pruning techniques on the representation learned by deep neural networks trained with contrastive loss functions. Our work finds that at high spars…