The paradigm shift from shallow classifiers with hand-crafted features to end-to-end trainable deep learning models has shown significant improvements on supervised learning tasks. Despite the promising power of deep neural networks (DNN), how to alleviate overfitting during training has been a research topic of interest. In this paper, we present a Generative-Discriminative Variational Model (GDVM) for visual classification, in which we introduce a latent variable inferred from inputs for exhibiting generative abilities towards prediction. In other words, our GDVM casts the supervised learning task as a generative learning process, with data discrimination to be jointly exploited for improved classification. In our experiments, we consider the tasks of multi-class classification, multi-label classification, and zero-shot learning. We show that our GDVM performs favorably against the baselines or recent generative DNN models.
@article{arxiv.1706.02295,
title = {Generative-Discriminative Variational Model for Visual Recognition},
author = {Chih-Kuan Yeh and Yao-Hung Hubert Tsai and Yu-Chiang Frank Wang},
journal= {arXiv preprint arXiv:1706.02295},
year = {2017}
}