← Back to all news
arXiv Machine Learning By Tiana C. Johnson, Donsub Rim

Adversarial Training Without Input Gradients via Low-Rank Householder Expansions

Read the original on arXiv Machine Learning →

The paper introduces a method for adversarial training that avoids computing input gradients by using a low‑rank Householder expansion (LRHE) to directly generate small‑norm adversarial examples from a network’s parameters. This approach requires only forward passes and standard back‑propagation, eliminating the inner maximization loop and reducing computational cost to roughly 2.8 PGD steps per epoch. The resulting models achieve comparable robustness to multi‑step PGD training for small relative ε budgets, demonstrating the feasibility of gradient‑free adversarial training.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Jun 2

Multigrade Neural Network Approximation

arXiv:2601. 16884v3 Announce Type: replace Abstract: We study multigrade deep learning (MGDL) as a principled framework for structured error refinement in deep neural networks.

By Shijun Zhang, Zuowei Shen, Yuesheng Xu
More like this →
arXiv Machine Learning
5d ago

Augmentation-Guided Adversarial Training via Local Stability-Based Sample Selection

The paper introduces Augmentation-Guided Adversarial Training (AGAT), a selective adversarial training method that uses augmentation-induced local variation to identify unstable samples for training. AGAT employs Augmentation-based Local Stability (ALS) to rank samples based on the normalized change in model logits between an input and its label-preserving augmented version. Experiments on MNIST, CIFAR-10, CIFAR-100, and Tiny-ImageNet-200 show that AGAT achieves comparable or higher robust accuracy while applying adversarial supervision to only 20‑30% of training samples per minibatch, and it requires less training time than other adversarial training baselines.

By Daniel Agyei Asante, Mark Tetteh, Deborah Asamoah, Bernard Asante, Regina Esi Turkson
safety
More like this →