44 citations · 83 across the 6 of their papers we have counts for
6 papers
Optimization for Robustness Evaluation beyond Metrics
Hengyue Liang, Buyun Liang, Ying Cui +2
Empirical evaluation of deep learning models against adversarial attacks entails solving nontrivial constrained optimization problems. Popular algorithms for solving these constrai…
Blind Image Deblurring with Unknown Kernel Size and Substantial Noise
Zhong Zhuang, Taihui Li, Hengkang Wang +1
Blind image deblurring (BID) has been extensively studied in computer vision and adjacent fields. Modern methods for BID can be grouped into two categories: single-instance methods…
NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning
Buyun Liang, Tim Mitchell, Ju Sun
Optimizing nonconvex (NCVX) problems, especially nonsmooth and constrained ones, is an essential part of machine learning. However, it can be hard to reliably solve such problems w…
Early Stopping for Deep Image Prior
Hengkang Wang, Taihui Li, Zhong Zhuang +3
Deep image prior (DIP) and its variants have showed remarkable potential for solving inverse problems in computer vision, without any extra training data. Practical DIP models are…
Self-Validation: Early Stopping for Single-Instance Deep Generative Priors
Taihui Li, Zhong Zhuang, Hengyue Liang +3
Recent works have shown the surprising effectiveness of deep generative models in solving numerous image reconstruction (IR) tasks, even without training data. We call these models…
Phase Retrieval using Single-Instance Deep Generative Prior
Kshitij Tayal, Raunak Manekar, Zhong Zhuang +4
Several deep learning methods for phase retrieval exist, but most of them fail on realistic data without precise support information. We propose a novel method based on single-inst…