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Found 4 papers, 1 papers with code
A Robust Likelihood Model for Novelty Detection
While this is a known problem that has been studied in recent years for the case of supervised learning, the case of novelty detection has received very limited attention.
Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis
In particular, we use average histogram matching (HM) and DL fine-tuning methods to generalize a FFDM model to the 2D maximum intensity projection (MIP) of DBT images.
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
We assume that training data is available to describe only the inlier distribution.
A Supervised Low-Rank Method for Learning Invariant Subspaces
Sparse representation and low-rank matrix decomposition approaches have been successfully applied to several computer vision problems.
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