Style encoding for class-specific image generation

Dana Cohen Hochberg; Raja Giryes; Hayit Greenspan

doi:10.1117/12.2580878

15 February 2021 Style encoding for class-specific image generation

Dana Cohen Hochberg, Raja Giryes, Hayit Greenspan

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 1159631 (2021) https://doi.org/10.1117/12.2580878
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

A fundamental problem in employing deep learning algorithms in the medical field is the lack of labeled data and severe class imbalance. In this work, we present novel ways to enlarge small scale datasets. We introduce an autoencoder framework comprised of an encoder and a StyleGAN generator to embed images into the latent space of StyleGAN. The autoencoder learns the disentangled latent representation of the data allowing for encoding real images to the latent space and manipulating the latent vector in a meaningful manner. We suggest ways to use the encoder along with the unique architecture of the StyleGAN generator to control the synthesized images and thus, create class-specific images that can be used to train and improve existing deep learning algorithms.

Conference Presentation

Citation Download Citation

Dana Cohen Hochberg, Raja Giryes, and Hayit Greenspan "Style encoding for class-specific image generation", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 1159631 (15 February 2021); https://doi.org/10.1117/12.2580878

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