Search Results for author:
Found 3 papers, 2 papers with code
Can we achieve robustness from data alone?
Adversarial training and its variants have come to be the prevailing methods to achieve adversarially robust classification using neural networks.
Domain-wise Adversarial Training for Out-of-Distribution Generalization
Extensive experiments show that our proposed DAT can effectively remove the domain-varying features and improve OOD generalization on both correlation shift and diversity shift tasks.
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot
In this paper, we conduct sanity checks for the above beliefs on several recent unstructured pruning methods and surprisingly find that: (1) A set of methods which aims to find good subnetworks of the randomly-initialized network (which we call "initial tickets"), hardly exploits any information from the training data; (2) For the pruned networks obtained by these methods, randomly changing the preserved weights in each layer, while keeping the total number of preserved weights unchanged per layer, does not affect the final performance.
