5,096 research outputs found
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
Training robust deep learning (DL) systems for disease detection from medical
images is challenging due to limited images covering different disease types
and severity. The problem is especially acute, where there is a severe class
imbalance. We propose an active learning (AL) framework to select most
informative samples for training our model using a Bayesian neural network.
Informative samples are then used within a novel class aware generative
adversarial network (CAGAN) to generate realistic chest xray images for data
augmentation by transferring characteristics from one class label to another.
Experiments show our proposed AL framework is able to achieve state-of-the-art
performance by using about of the full dataset, thus saving significant
time and effort over conventional methods
Guiding InfoGAN with Semi-Supervision
In this paper we propose a new semi-supervised GAN architecture (ss-InfoGAN)
for image synthesis that leverages information from few labels (as little as
0.22%, max. 10% of the dataset) to learn semantically meaningful and
controllable data representations where latent variables correspond to label
categories. The architecture builds on Information Maximizing Generative
Adversarial Networks (InfoGAN) and is shown to learn both continuous and
categorical codes and achieves higher quality of synthetic samples compared to
fully unsupervised settings. Furthermore, we show that using small amounts of
labeled data speeds-up training convergence. The architecture maintains the
ability to disentangle latent variables for which no labels are available.
Finally, we contribute an information-theoretic reasoning on how introducing
semi-supervision increases mutual information between synthetic and real data
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