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![1
DEEP LEARNING JP
[DL Papers]
http://deeplearning.jp/
Disentangling Disentanglement
Hiroshi Sekiguchi, Morikawa Lab](https://image.slidesharecdn.com/20190405dldisentanglingdisentanglementver1-190412005025/85/DL-Disentangling-Disentanglement-1-320.jpg?cb=1555030252)
![一般化の導入（２）
• 目的関数:
– 新提案の場合：
𝐿α,β(𝑥) = 𝐿β(𝑥) − α 𝐷(𝑞Φ 𝑧 , 𝑝 𝑧 )
= 𝐸 𝑞Φ 𝑧 𝑥 [log 𝑝θ(𝑥|𝑧)] − 𝛽 𝐾𝐿(𝑞Φ(𝑧|𝑥)||𝑝θ 𝑧 ) − α 𝐷(𝑞Φ 𝑧 ...](https://image.slidesharecdn.com/20190405dldisentanglingdisentanglementver1-190412005025/85/DL-Disentangling-Disentanglement-7-320.jpg?cb=1555030252)
![実験結果（２）
• 実験２）新提案による周辺化事後分布𝑞Φ 𝑧 = 𝐸 𝑝 𝐴(𝑥)
[𝑞Φ 𝑧 𝑥 ]が事前
分布𝑝(𝑧)を表現する例
– 手法： 新手法：αとβを変化
– 事前確率：４等重み付けガウシアン
– データセット：
• Pinwh...](https://image.slidesharecdn.com/20190405dldisentanglingdisentanglementver1-190412005025/85/DL-Disentangling-Disentanglement-10-320.jpg?cb=1555030252)
![まとめ
• βーVAEの目的関数に、周辺化事後確率𝑞Φ 𝑧 = 𝐸 𝑝 𝐴(𝑥)
[𝑞Φ 𝑧 𝑥 ]と、事前
確率𝑝(𝑧)の差異を少なくする正則化を追加して、実世界の事前確率を学
習する手法を提案した。
11](https://image.slidesharecdn.com/20190405dldisentanglingdisentanglementver1-190412005025/85/DL-Disentangling-Disentanglement-11-320.jpg?cb=1555030252)
