Search Results for author: Heeyoung Kim

Found 4 papers, 3 papers with code

CDMAD: Class-Distribution-Mismatch-Aware Debiasing for Class-Imbalanced Semi-Supervised Learning

1 code implementation CVPR 2024 Hyuck Lee, Heeyoung Kim

Pseudo-label-based semi-supervised learning (SSL) algorithms trained on a class-imbalanced set face two cascading challenges: 1) Classifiers tend to be biased towards majority classes, and 2) Biased pseudo-labels are used for training.

Pseudo Label

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.

Out-of-Distribution Detection Out of Distribution (OOD) 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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