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Found 2 papers, 1 papers with code
Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning
Unsupervised meta-learning aims to learn generalizable knowledge across a distribution of tasks constructed from unlabeled data.
AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection
To break through these observations, we propose a simple solution AltUB which introduces alternating training to update the base distribution of normalizing flow for anomaly detection.
Ranked #2 on
Anomaly Detection
on BTAD
(using extra training data)
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