activity
20192022
most citedCDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and Demosaicing

33 citations · 53 across the 11 of their papers we have counts for

collaborators
Showing 2021Show all

7 papers · 1 filter

eess.IV2021★ 33 cited

CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and Demosaicing

Nikola Janjušević, Amirhossein Khalilian-Gourtani, Yao Wang

Deep learning based methods hold state-of-the-art results in low-level image processing tasks, but remain difficult to interpret due to their black-box construction. Unrolled optim…

cs.CV2021★ 2 cited

Domain Adaptation of Networks for Camera Pose Estimation: Learning Camera Pose Estimation Without Pose Labels

Jack Langerman, Ziming Qiu, Gábor Sörös +3

One of the key criticisms of deep learning is that large amounts of expensive and difficult-to-acquire training data are required in order to train models with high performance and…

cs.LG2021★ 2 cited

Nyström Regularization for Time Series Forecasting

Zirui Sun, Mingwei Dai, Yao Wang +1

This paper focuses on learning rate analysis of Nyström regularization with sequential sub-sampling for -mixing time series. Using a recently developed Banach-valued Bernstein i…

cs.LG2021★ 1 cited

Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets

Shao-Bo Lin, Yao Wang, Ding-Xuan Zhou

In this paper, we study the generalization performance of global minima for implementing empirical risk minimization (ERM) on over-parameterized deep ReLU nets. Using a novel deepe…

cs.LG2021

Universal Consistency of Deep Convolutional Neural Networks

Shao-Bo Lin, Kaidong Wang, Yao Wang +1

Compared with avid research activities of deep convolutional neural networks (DCNNs) in practice, the study of theoretical behaviors of DCNNs lags heavily behind. In particular, th…

eess.IV2021★ 3 cited

CDLNet: Robust and Interpretable Denoising Through Deep Convolutional Dictionary Learning

Nikola Janjušević, Amirhossein Khalilian-Gourtani, Yao Wang

Deep learning based methods hold state-of-the-art results in image denoising, but remain difficult to interpret due to their construction from poorly understood building blocks suc…