4. Background|ドメインシフトの対策
Domain Adaptation Test Time Adaptation (TTA)
Adaptation
&
Test
Source
data
Target
data
Train Time Test Time
訓練時にテスト時のデータを利用して
モデルを適応しながら訓練する
訓練時にテスト時の撮影状況
を把握している必要がある
訓練時は訓練データのみを利用
テスト時にモデルを適応しながら推論
訓練時にテスト時の撮影状況
を知る必要がない
撮影状況の変化
Training
&
Adaptation
Source
data
Target
data
Test
Train Time Test Time 撮影状況の変化
2024/1/24 4
5. Agenda|Test Time Adaptation
Improving robustness against common corruptions by
covariate shift adaptation,
Steffen Schneider+(University of Tübingen & IMPRS-IS)[NeurIPS‘20]
TENT: Fully Test-Time Adaptation by Entropy Minimization,
Dequan Wang, Evan Shelhamer+ (UC Berkeley, Adobe) [ICLR’21]
(被引用数:511)
(被引用数:280)
TTN: A Domain-Shift Aware Batch Normalization in Test-
Time Adaptation,
Hyesu Lim, Byeonggeun Kim+ (Qualcomm AI Research) [ICLR’23] (被引用数:17)
On Pitfalls of Test-Time Adaptation,
Hao Zhao, Yuejiang Liu+ (EPFL) [ICML’23] (被引用数:4)
2024/1/24 5
6. Improving robustness against common corruptions by covariate
shift adaptation,
Steffen Schneider+(University of Tübingen & IMPRS-IS)[NeurIPS‘20]
2024/1/24 6
バッチ統計量をテストデータで適応
(被引用数:280)
Test Time Adaptation の先駆けとなる研究
Conv BN Conv BN Linear dog
統計量(平均・分散)はドメイン
固有な情報と捉えられる
Weight (trainable)
Bias (trainable)
Running_mean (statistic)
Running_var (statistic)
バッチ正規化層に着目
8. Improving robustness against common corruptions by
covariate shift adaptation,
Steffen Schneider+(University of Tübingen & IMPRS-IS)[NeurIPS‘20]
2024/1/24 8
(被引用数:280)
手法
N:ハイパラ
𝑛:𝑠𝑎𝑚𝑝𝑙𝑒 𝑠𝑖𝑧𝑒
9. Improving robustness against common corruptions by covariate
shift adaptation,
Steffen Schneider+(University of Tübingen & IMPRS-IS)[NeurIPS‘20]
2024/1/24 9
ImageNet-Cなどでモデルの改善
(被引用数:280)
10. TENT: Fully Test-Time Adaptation by Entropy Minimization,
Dequan Wang, Evan Shelhamer+ (UC Berkeley, Adobe) [ICLR’21]
2024/1/24 10
Test Time Adaptationタスクを定義
(被引用数:511)