Title: Deep Multiple Instance Feature Learning via Variational Autoencoder,
Abstract: 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 regularized by variational
autoencoders (VAEs) to maximize the differences between latent representations
of all instances and negative instances. As a result, the hidden layer of the
variational autoencoder learns meaningful representation. This representation
can effectively be used for MIL problems as illustrated by better performance
on the standard benchmark datasets comparing to the state-of-the-art
approaches. More importantly, unlike most related studies, the proposed
framework can be easily scaled to large dataset problems, as illustrated by the
audio event detection and segmentation task. Visualization also confirms the
effectiveness of the latent representation in discriminating positive and
negative classes.