Models and code are available at https://github.com/hujie-frank/SENet. Jie Hu, Li Shen, et al., "Squeeze-and-Excitation Networks" https://arxiv.org/abs/1709.01507 CNNの汎化性能を改善するために、特徴マップのチャンネルごとに重み付けして次の…

Squeeze-and-Excitation Networks formed the foundation of our ILSVRC 2017 classification submission which won first place and reduced the top-5 error to 2.251%, surpassing the winning entry of 2016 by a relative improvement of ∼25%. Jie Hu,…

We further demonstrate that SE blocks bring significant improvements in performance for existing state-of-the-art CNNs at slight additional computational cost. Jie Hu, Li Shen, et al., "Squeeze-and-Excitation Networks" https://arxiv.org/ab…

We show that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. Jie Hu, Li Shen, et al., "Squeeze-and-Excitation Networks" https://arxiv.org/abs/1709.01507 CNN…

In this work, we focus instead on the channel relationship and propose a novel architectural unit, which we term the “Squeeze-and-Excitation” (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling i…

A broad range of prior research has investigated the spatial component of this relationship, seeking to strengthen the representational power of a CNN by enhancing the quality of spatial encodings throughout its feature hierarchy. Jie Hu, …

The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information within local receptive fields at e…