8 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.CV2023
Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images
Nam Cao, Olga Saukh
Distribution shifts are characterized by differences between the training and test data distributions. They can significantly reduce the accuracy of machine learning models deploye…
cs.CV2023★ 8 cited
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…
cs.LG2022
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…