16 citations · 34 across the 6 of their papers we have counts for
10 papers · 1 filter
Active Domain Adaptation with Multi-level Contrastive Units for Semantic Segmentation
Hao Zhang, Ruimao Zhang, Zhanglin Peng +2
To further reduce the cost of semi-supervised domain adaptation (SSDA) labeling, a more effective way is to use active learning (AL) to annotate a selected subset with specific pro…
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
Andrey Ignatov, Radu Timofte, Zhilu Zhang +36
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world RAW-to…
Exemplar Normalization for Learning Deep Representation
Ruimao Zhang, Zhanglin Peng, Lingyun Wu +2
Normalization techniques are important in different advanced neural networks and different tasks. This work investigates a novel dynamic learning-to-normalize (L2N) problem by prop…
Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks
Zhaoyang Zhang, Jingyu Li, Wenqi Shao +4
Group convolution, which divides the channels of ConvNets into groups, has achieved impressive improvement over the regular convolution operation. However, existing models, eg. Res…
Switchable Normalization for Learning-to-Normalize Deep Representation
Ping Luo, Ruimao Zhang, Jiamin Ren +2
We address a learning-to-normalize problem by proposing Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neu…
Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct?
Ping Luo, Zhanglin Peng, Jiamin Ren +1
Yes, they do. This work investigates a perspective for deep learning: whether different normalization layers in a ConvNet require different normalizers. This is the first step towa…