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cs.LG2024
Effective and Robust Adversarial Training against Data and Label Corruptions
Peng-Fei Zhang, Zi Huang, Xin-Shun Xu +1
Corruptions due to data perturbations and label noise are prevalent in the datasets from unreliable sources, which poses significant threats to model training. Despite existing eff…
cs.LG2024
PAODING: A High-fidelity Data-free Pruning Toolkit for Debloating Pre-trained Neural Networks
Mark Huasong Meng, Hao Guan, Liuhuo Wan +3
We present PAODING, a toolkit to debloat pretrained neural network models through the lens of data-free pruning. To preserve the model fidelity, PAODING adopts an iterative process…