April 2020
Intermediate to advanced
344 pages
8h 22m
Japanese
Content preview from Pythonではじめる教師なし学習 ―機械学習の可能性を広げるラベルなしデータの利用
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264
12
章 敵対的生成ネットワーク
12.1.1
GAN
の威力
「11 章 深層信念ネットワークを用いた特徴量検出」で、
DBN
などの教師なし学習モデルの作る合
成データを使って、教師あり学習モデルの性能を向上させる方法を紹介した。
DBN
と同様に、
GAN
も合成データの生成が非常に得意だ。
例えば画像識別タスクの性能を向上させるために、既存のデータに追加する訓練データを大量に生
成したいなら、生成ネットワークを用いて大量の合成データを作り、それを元の訓練データに足して、
大きなデータセットを作って、教師あり学習モデルを訓練すれば良い。
GAN
は異常検出にも有用だ。例えば不正や侵入などの怪しい挙動の検出などの、異常の特定が行
いたいなら、識別ネットワークを用いて実際のデータにスコアを付ければいい。識別ネットワークが「合
成データらしい」と判断したものは、異常だということなので、悪意のある挙動である可能性が高い。
12.2
深層畳み込み敵対的生成ネットワーク
本章では、本書の前半でも用いた
MNIST
データセットに立ち戻り、
GAN
の一種を用いて、合成デー
タを生成して
MNIST
データセットを補足する。そして、教師あり学習モデルを用いて画像クラス分類
を行う。これも、半教師あり学習の
1
つだ。
余談になるが、ここまで読んだ読者は半教師あり学習の利点がわかっていることだろう。世
界に存在するデータの多くにはラベルが付いていないので、ラベル付けを効率よく行う教師
なし学習の能力はそれ自体非常に強力だ。今日の産業的に成功 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.