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UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation

Autor: Ji, Yuanfeng, Zhang, Ruimao, Li, Zhen, Ren, Jiamin, Zhang, Shaoting, Luo, Ping

Aggregating multi-level feature representation plays a critical role in achieving robust volumetric medical image segmentation, which is important for the auxiliary diagnosis and treatment. Unlike the recent neural architecture search (NAS) methods t

Externí odkaz: http://arxiv.org/abs/2009.07501

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Switchable Normalization for Learning-to-Normalize Deep Representation

Autor: Luo, Ping, Zhang, Ruimao, Ren, Jiamin, Peng, Zhanglin, Li, Jingyu

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 neural network. SN employs three distinct scopes to compute statistics (m

Externí odkaz: http://arxiv.org/abs/1907.10473

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Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct?

Autor: Luo, Ping, Peng, Zhanglin, Ren, Jiamin, Zhang, Ruimao

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 towards understanding this phenomenon. We allow each convolutional layer t

Externí odkaz: http://arxiv.org/abs/1811.07727

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