Less is More: Data Pruning for Faster Adversarial Training

no code implementations • 23 Feb 2023 • Yize Li, Pu Zhao, Xue Lin, Bhavya Kailkhura, Ryan Goldhahn

Deep neural networks (DNNs) are sensitive to adversarial examples, resulting in fragile and unreliable performance in the real world.

Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed

no code implementations • 26 Sep 2022 • Hao Cheng, Pu Zhao, Yize Li, Xue Lin, James Diffenderfer, Ryan Goldhahn, Bhavya Kailkhura

Recently, Diffenderfer and Kailkhura proposed a new paradigm for learning compact yet highly accurate binary neural networks simply by pruning and quantizing randomly weighted full precision neural networks.

Reverse Engineering of Imperceptible Adversarial Image Perturbations

2 code implementations • ICLR 2022 • Yifan Gong, Yuguang Yao, Yize Li, Yimeng Zhang, Xiaoming Liu, Xue Lin, Sijia Liu

However, carefully crafted, tiny adversarial perturbations are difficult to recover by optimizing a unilateral RED objective.

Data Augmentation Image Denoising