Search Results for author: Heeyoung Kim

Found 3 papers, 2 papers with code

Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models

1 code implementation NeurIPS 2021 keunseo kim, JunCheol Shin, Heeyoung Kim

Several out-of-distribution (OOD) detection scores have been recently proposed for deep generative models because the direct use of the likelihood threshold for OOD detection has been shown to be problematic.

OOD Detection Out-of-Distribution Detection

ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning

1 code implementation NeurIPS 2021 Hyuck Lee, Seungjae Shin, Heeyoung Kim

The ABC is trained with a class-balanced loss of a minibatch, while using high-quality representations learned from all data points in the minibatch using the backbone SSL algorithm to avoid overfitting and information loss. Moreover, we use consistency regularization, a recent SSL technique for utilizing unlabeled data in a modified way, to train the ABC to be balanced among the classes by selecting unlabeled data with the same probability for each class.

OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification

no code implementations NeurIPS 2020 Taewon Jeong, Heeyoung Kim

The joint analysis of N sub-tasks facilitates simultaneous classification and OOD detection and, furthermore, offers an advantage, in that it does not require re-training when the number of classes for a test task differs from that for training tasks; it is sufficient to simply assume as many sub-tasks as the number of classes for the test task.

Few-Shot Learning General Classification +2

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