Zero-shot learning via class-conditioned deep generative models
Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. We present a deep generative model for Zero-Shot Learning (ZSL). Unlike most existing methods for this problem, that represent each class as a point (via a semantic embedding), we represent each seen/unseen class using a class-specific latent-space distribution, conditioned on class attributes. We use these latent-space distributions as a prior for a supervised variational autoencoder (VAE), which also facilitates learning highly discriminative feature representations for the inputs. The entire framework is learned end-to-end using only the seen-class training data. At test time, the label for an unseen-class test input is the class that maximizes the VAE lower bound. We further extend the model to a (i) semi-supervised/transductive setting by leveraging unlabeled unseen-class data via an unsupervised learning module, and (ii) few-shot learning where we also have a small number of labeled inputs from the unseen classes. We compare our model with several state-of-the-art methods through a comprehensive set of experiments on a variety of benchmark data sets.
Wang, W; Pu, Y; Verma, VK; Fan, K; Zhang, Y; Chen, C; Rai, P; Carin, L
32nd Aaai Conference on Artificial Intelligence, Aaai 2018
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International Standard Book Number 13 (ISBN-13)