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Image data augmentation 画像認識の Deep Learning で、データ拡張を行う時の注意点
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Image data augmentation_v05
1.
画像認識の Deep Learning
で、データ拡張を行う時の注意点 中野和俊
2.
自己紹介 中野和俊(@kznx) ワーク:株式会社ALBERTでプロダクトマネージャー ライフ:ポケモン Go を地道に継続し、Lv38 twitter
: @kznx Facebook : https://www.facebook.com/nakano.kazutoshi (本資料は個人の見解であり、所属する組織の公式見解ではありません)
3.
AIの実社会適用での現実と課題 ・一般論:画像認識の Deep Learning
では、大量に学習用の画像データが必要 ・現実 :実社会では、画像データを大量に集めるのは、困難な場合が多い ・課題 :少ない画像データでなんとかできないか
4.
解決策 ・データ拡張(水増し) ・転移学習
5.
データ拡張(水増し) ・Data Augmentation ・元の画像を変形させてバリエーションを増やすこと ・変形方法 回転、平行移動、縮小、拡大、切り抜き、色調変更、上下左右反転 コントラスト変更、明るさ変更、・・・ (デモ)
6.
注意点 ・過学習しやすい ・意味が無い拡張 ・実社会で使われるケースを意識する
7.
まとめ ・枯れた技術ですが、正しく使えば効果的 ・データの意味や背景を考えることが重要 ・CG, GAN で画像生成 参考
https://www.slideshare.net/siliconstudio/cg-79508015 ・本日の資料はslideshareにあげておきます https://www.slideshare.net/KazutoshiNakano
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