3 papers
cs.LG2024
Few-shot Adaptation to Distribution Shifts By Mixing Source and Target Embeddings
Yihao Xue, Ali Payani, Yu Yang +1
Pretrained machine learning models need to be adapted to distribution shifts when deployed in new target environments. When obtaining labeled data from the target distribution is e…
cs.CV2024
Sieve: Multimodal Dataset Pruning Using Image Captioning Models
Anas Mahmoud, Mostafa Elhoushi, Amro Abbas +4
Vision-Language Models (VLMs) are pretrained on large, diverse, and noisy web-crawled datasets. This underscores the critical need for dataset pruning, as the quality of these data…
cs.LG2024
Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias
Yu Yang, Eric Gan, Gintare Karolina Dziugaite +1
Neural networks trained with (stochastic) gradient descent have an inductive bias towards learning simpler solutions. This makes them highly prone to learning spurious correlations…