Squeeze and-excitation networks

•

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shima o

Summary of https://arxiv.org/abs/1709.01507

Sciences

Squeeze‐and‐Excitation Networks,
Hu+, '17

概要
チャネル間の相互依存性を明示的に加えることでネットワークの表現力を上げる構造の
提案
Squeeze、Excitationの2つの機構を追加
既存の手法に追加するだけでよい
パラメタ数の増加は微量
ImageNetでSoTA

手法
Squeeze‐and‐Excitation Block（see Fig2,3）
F : transformation=Conv.層
F : sqeeze=Global average poolingで1×1×Cへ
F : Excitation=bottleneck構造（パラメタrでくびれ率を決める）
ここでチャネル間の関係性を考慮したweightを算出
: Uの各チャネルにF で算出したスカラ値s をかける
tr
sq
ex
X
~
ex c

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Squeeze and-excitation networks

1. Squeeze‐and‐Excitation Networks, Hu+, '17

2. Agenda 概要手法実験/結果

3. 概要チャネル間の相互依存性を明示的に加えることでネットワークの表現力を上げる構造の提案 Squeeze、Excitationの2つの機構を追加既存の手法に追加するだけでよいパラメタ数の増加は微量 ImageNetでSoTA

4. 手法 Squeeze‐and‐Excitation Block（see Fig2,3） F : transformation=Conv.層 F : sqeeze=Global average poolingで1×1×Cへ F : Excitation=bottleneck構造（パラメタrでくびれ率を決める）ここでチャネル間の関係性を考慮したweightを算出 : Uの各チャネルにF で算出したスカラ値s をかける tr sq ex X ~ ex c

5. Squeeze‐and‐Excitation Block

6. Squeeze‐and‐Excitation Block

7. 実験結果1 SE構造を導入しても処理速度はほぼ変わらない

8. 実験結果2