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Paper Review/Out-of-Distribution Detection (OOD) 2

[CVPR 2023] GEN: Pushing the Limits of Softmax-Based Out-of-Distribution Detection

- Introduction OOD detection scenarios Covariate shift: Change in the input distribution Semantic shift: Change in the label distribution Existing OOD detection works Predictive distribution Incorporate feature statistics for ID data Requires a portion of training data Internal feature activation GOAL: Explore and push the limits of OOD detection when the output of a softmax layer is the only av..

Paper Review/Out-of-Distribution Detection (OOD) 2023.07.04

[ECCV 2022] DICE: Leveraging Sparsification for Out-of-Distribution Detection

- Introduction OOD detection 의 main challenge Deep neural network 가 OOD sample 에 대해서 overconfident 한 prediction 을 보임 ID (In-Distribution) 과 OOD 의 구분을 어렵게 만듦 기존 OOD 기법들은 sparcification 을 간과하며 오직 overparameterized weight 만을 이용하여 OOD score 를 계산 저자들은 중요하지 않은 unit 과 weight 에 의존하는것이 OOD detection 을 취약하게 만들 수 있다고 주장 ID 인 CIFAR10 에 학습된 network 가 OOD 인 SVHN 의 데이터가 입력으로 들어왔을 때 무시할 수 없을 정도의 unit 을 penultim..

Paper Review/Out-of-Distribution Detection (OOD) 2023.03.17
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