3 papers
cs.LG2022
Leveraging Unlabeled Data to Track Memorization
Mahsa Forouzesh, Hanie Sedghi, Patrick Thiran
Deep neural networks may easily memorize noisy labels present in real-world data, which degrades their ability to generalize. It is therefore important to track and evaluate the ro…
cs.LG2021
Disparity Between Batches as a Signal for Early Stopping
Mahsa Forouzesh, Patrick Thiran
We propose a metric for evaluating the generalization ability of deep neural networks trained with mini-batch gradient descent. Our metric, called gradient disparity, is the $\ell_…
cs.LG2020
Generalization Comparison of Deep Neural Networks via Output Sensitivity
Mahsa Forouzesh, Farnood Salehi, Patrick Thiran
Although recent works have brought some insights into the performance improvement of techniques used in state-of-the-art deep-learning models, more work is needed to understand the…