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Deep Multiple Instance Feature Learning via Variational Autoencoder
[article]
2018
arXiv
pre-print
We describe a novel weakly supervised deep learning framework that combines both the discriminative and generative models to learn meaningful representation in the multiple instance learning (MIL) setting. MIL is a weakly supervised learning problem where labels are associated with groups of instances (referred as bags) instead of individual instances. To address the essential challenge in MIL problems raised from the uncertainty of positive instances label, we use a discriminative model
arXiv:1807.02490v1
fatcat:fkbqveoz5fdlrixaizssgben5q