Learning to Generalize Provably in Learning to Optimize

1 code implementation • 22 Feb 2023 • Junjie Yang, Tianlong Chen, Mingkang Zhu, Fengxiang He, DaCheng Tao, Yingbin Liang, Zhangyang Wang

While the optimizer generalization has been recently studied, the optimizee generalization (or learning to generalize) has not been rigorously studied in the L2O context, which is the aim of this paper.

Generalizable Learning to Optimize into Wide Valleys

no code implementations • 29 Sep 2021 • Junjie Yang, Tianlong Chen, Mingkang Zhu, Fengxiang He, DaCheng Tao, Yingbin Liang, Zhangyang Wang

Learning to optimize (L2O) has gained increasing popularity in various optimization tasks, since classical optimizers usually require laborious, problem-specific design and hyperparameter tuning.

Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm

1 code implementation • 10 Jun 2021 • Mingkang Zhu, Tianlong Chen, Zhangyang Wang

Compared to state-of-the-art methods, our homotopy attack leads to significantly fewer perturbations, e. g., reducing 42. 91% on CIFAR-10 and 75. 03% on ImageNet (average case, targeted attack), at similar maximal perturbation magnitudes, when still achieving 100% attack success rates.

Adversarial Attack

Learning Transferable 3D Adversarial Cloaks for Deep Trained Detectors

1 code implementation • 22 Apr 2021 • Arman Maesumi, Mingkang Zhu, Yi Wang, Tianlong Chen, Zhangyang Wang, Chandrajit Bajaj

This paper presents a novel patch-based adversarial attack pipeline that trains adversarial patches on 3D human meshes.

Adversarial Attack Object