Semi-Supervised Variational Autoencoders for Out-of-Distribution Generation
Frantzeska Lavda,
Alexandros Kalousis
Abstract:Humans are able to quickly adapt to new situations, learn effectively with limited data, and create unique combinations of basic concepts. In contrast, generalizing out-of-distribution (OOD) data and achieving combinatorial generalizations are fundamental challenges for machine learning models. Moreover, obtaining high-quality labeled examples can be very time-consuming and expensive, particularly when specialized skills are required for labeling. To address these issues, we propose BtVAE, a method that utiliz… Show more
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