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[Dl輪読会]Censoring Representation with Adversary

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Deep Learning JP
Deep Learning JP

2016/9/2 Deep Learning JP: http://deeplearning.jp/seminar-2/

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Censoring Representation with Adversary D3岩澤
書誌情報 • ICLR2016 • Harrison Edwards & Amos Storkey • エディンバラ大学 • Citation:1 • 余談：5月にアップデートされて文がだいぶ読みやすくなって る
概要 • GANで利用されているAdversarial Trainingを画像生成以外に 使う • 具体的には，表現に含まれるべきでない情報を取り出す • 含まれるべきでない例： • Fairness：黒人/白人という情報で推薦結果を変えない • Image Anonymization：画像中に含まれる住所や氏名を消したい
Fairness, Image Anonymizationの難しさ • 例えば黒人/白人に依存しない推薦結果を与えたい場合を考える • この時、特徴にある黒人/白人という特徴量を落とせば一見良さ そうだがそれでは不十分 • なぜなら、他の特徴量が黒人、白人に関する情報量を持ってる 可能性があるから • 素性が、黒人/白人に依存しないようにする必要がある
Fairness, Image Anonymizationの難しさ2 • データの価値とFairness/プライバシーは究極的に対立する • 画像から機微情報を消したい場合 • 究極的には入力に依存しない特徴量を返す関数fを使えば機微情 報は消える • がデータの価値も同時に損なわれる • データの価値とプライバシー保護のバランスをとることが大事
提案手法 • 上を最適化 • 再構築誤差とクラス分類誤差は普通の誤差なので Fairness/Privacyを説明する • 以降Sとしては2値を考える • 例：黒人か白人か、機微情報を含むか含まないか 再構築誤差 Fairness/Privacy クラス分類誤差
D(S,R)の学習: Adversarial Training
アルゴリズム全体
• 興味持った方は，結果などは論文を参照したください! • https://arxiv.org/abs/1511.05897

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