Semi-Supervised Learning with Generative Adversarial Networks
read the original abstract
We extend Generative Adversarial Networks (GANs) to the semi-supervised context by forcing the discriminator network to output class labels. We train a generative model G and a discriminator D on a dataset with inputs belonging to one of N classes. At training time, D is made to predict which of N+1 classes the input belongs to, where an extra class is added to correspond to the outputs of G. We show that this method can be used to create a more data-efficient classifier and that it allows for generating higher quality samples than a regular GAN.
This paper has not been read by Pith yet.
Forward citations
Cited by 2 Pith papers
-
Multimodal Deep Generative Model for Semi-Supervised Learning under Class Imbalance
A multimodal generative model replaces Gaussians with t-distributions and uses gamma-power divergence to improve semi-supervised classification performance on imbalanced partially labeled data.
-
Hard-Aware Fashion Attribute Classification
Presents HABP to emphasize hard samples during training and Deact to generate stable synthetic samples for rare attributes, outperforming prior methods on large-scale fashion datasets without extra supervision.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.