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NeurIPS 2019 論文読み会
Fast AutoAugment
Inoue Yuichi
(@inoichan)
- データセットを見て、人がどのAugmentationをするか考えていた...!
引用: albumentations-team/albumentations
Augmentation...
AutoAugment (2017,Ekin D.Cubuk et al.)
- 強化学習を使ってデータセットに最適なAugmentationを探索
- コントローラRNNでAugmentationの組み合わせをサンプリング
Augmentationしたデータでモデルを学習
検証データの精度を報酬として与えてコントローラRNNを更新
- SOTAを更新した!
- 他のデータセットでも有効なAugmentation policy (transfer)
AutoAugment (2017,Ekin D.Cubuk et al.)
AutoAugment (2017,Ekin D.Cubuk et al.)
しかし、学習に膨大な計算リソースが必要!!!
Fast AutoAugmentの工夫点
❖ ベイズ最適化で探索
➢ どの処理をするか
➢ どのくらいの確率でするか
➢ どのくらいの強度で処理をするか
❖ Augmentationしてないデータでモデルを学習し、
推論時に精度が良くなるAugmentation policyを探す。
Fast AutoAugmentの処理
1個分のAugmentation処理
Fast AutoAugmentの処理
p : Augmentation処理をする確率
λ : 各Augmentation処理の強度
(magnitude)
Fast AutoAugmentの処理
各画像にNτ種類のAugmentation処理をする
Fast AutoAugmentの処理
Fast AutoAugmentの処理(イメージ図)
Fast AutoAugmentのアルゴリズム
Fast AutoAugmentのアルゴリズム
訓練データをK分割する(Stratified)
各Foldでさらに
DM : モデル訓練
DA : Augmentationしてモデル評価
に分ける。
Fast AutoAugmentのアルゴリズム
DMでモデルをスクラッチから学習
Fast AutoAugmentのアルゴリズム
DAをAugmentationして推論する。
Lossが小さくなるように以下を探索
- Augmentationの取り方
- 各Augmentationの確率p
- 各Augmentationの強度λ
T回探索を繰り返す。
Fast AutoAugmentのアルゴリズム
上位N個のAugmentation policyを選び、
最終的なAugmentationのリストに加える。
Fast AutoAugmentのアルゴリズム(イメージ図)
最終的にはこれでモデルを訓練する!
計算コストの削減に成功
AutoAugmentよりはるかに早く計算できてる!(GPU hours)
従来手法に匹敵する精度
Thanks!!!
Link:
Fast AutoAugment
arXive: https://arxiv.org/abs/1905.00397
Github: https://github.com/kakaobrain/fast-autoaugment
AutoAugment
arXive: https://arxiv.org/abs/1805.09501
                                       (図はこれらの論文から引用してます。)

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Fast AutoAugment