ArtGAN: Artwork Synthesis with Conditional Categorical GANs
arXiv:1702.03410
Abstract
This paper proposes an extension to the Generative Adversarial Networks (GANs), namely as ARTGAN to synthetically generate more challenging and complex images such as artwork that have abstract characteristics. This is in contrast to most of the current solutions that focused on generating natural images such as room interiors, birds, flowers and faces. The key innovation of our work is to allow back-propagation of the loss function w.r.t. the labels (randomly assigned to each generated images) to the generator from the discriminator. With the feedback from the label information, the generator is able to learn faster and achieve better generated image quality. Empirically, we show that the proposed ARTGAN is capable to create realistic artwork, as well as generate compelling real world images that globally look natural with clear shape on CIFAR-10.
arXiv comment10 pages, 10 figures, submitted to ICIP2017 (extension version)
References in corpus (7)
- Conditional Generative Adversarial Nets
- Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
- Autoencoding beyond pixels using a learned similarity metric
- A note on the evaluation of generative models
- Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
- Neural Photo Editing with Introspective Adversarial Networks
- Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature
Cited by in corpus (4)
- Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
- Biases in Generative Art -- A Causal Look from the Lens of Art History
- Quantifying Confounding Bias in Generative Art: A Case Study
- Autonomy, Authenticity, Authorship and Intention in computer generated art