8 citations · 17 across the 3 of their papers we have counts for
5 papers
UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation
Yuanfeng Ji, Ruimao Zhang, Zhen Li +3
Aggregating multi-level feature representation plays a critical role in achieving robust volumetric medical image segmentation, which is important for the auxiliary diagnosis and t…
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…
SSN: Learning Sparse Switchable Normalization via SparsestMax
Wenqi Shao, Tianjian Meng, Jingyu Li +4
Normalization methods improve both optimization and generalization of ConvNets. To further boost performance, the recently-proposed switchable normalization (SN) provides a new per…
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…
Differentiable Learning-to-Normalize via Switchable Normalization
Ping Luo, Jiamin Ren, Zhanglin Peng +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…