262 research outputs found
Which Training Methods for GANs do actually Converge?
Recent work has shown local convergence of GAN training for absolutely
continuous data and generator distributions. In this paper, we show that the
requirement of absolute continuity is necessary: we describe a simple yet
prototypical counterexample showing that in the more realistic case of
distributions that are not absolutely continuous, unregularized GAN training is
not always convergent. Furthermore, we discuss regularization strategies that
were recently proposed to stabilize GAN training. Our analysis shows that GAN
training with instance noise or zero-centered gradient penalties converges. On
the other hand, we show that Wasserstein-GANs and WGAN-GP with a finite number
of discriminator updates per generator update do not always converge to the
equilibrium point. We discuss these results, leading us to a new explanation
for the stability problems of GAN training. Based on our analysis, we extend
our convergence results to more general GANs and prove local convergence for
simplified gradient penalties even if the generator and data distribution lie
on lower dimensional manifolds. We find these penalties to work well in
practice and use them to learn high-resolution generative image models for a
variety of datasets with little hyperparameter tuning.Comment: conferenc
LoGAN: Generating Logos with a Generative Adversarial Neural Network Conditioned on color
Designing a logo is a long, complicated, and expensive process for any
designer. However, recent advancements in generative algorithms provide models
that could offer a possible solution. Logos are multi-modal, have very few
categorical properties, and do not have a continuous latent space. Yet,
conditional generative adversarial networks can be used to generate logos that
could help designers in their creative process. We propose LoGAN: an improved
auxiliary classifier Wasserstein generative adversarial neural network (with
gradient penalty) that is able to generate logos conditioned on twelve
different colors. In 768 generated instances (12 classes and 64 logos per
class), when looking at the most prominent color, the conditional generation
part of the model has an overall precision and recall of 0.8 and 0.7
respectively. LoGAN's results offer a first glance at how artificial
intelligence can be used to assist designers in their creative process and open
promising future directions, such as including more descriptive labels which
will provide a more exhaustive and easy-to-use system.Comment: 6 page, ICMLA1
Generating Multi-Categorical Samples with Generative Adversarial Networks
We propose a method to train generative adversarial networks on mutivariate
feature vectors representing multiple categorical values. In contrast to the
continuous domain, where GAN-based methods have delivered considerable results,
GANs struggle to perform equally well on discrete data. We propose and compare
several architectures based on multiple (Gumbel) softmax output layers taking
into account the structure of the data. We evaluate the performance of our
architecture on datasets with different sparsity, number of features, ranges of
categorical values, and dependencies among the features. Our proposed
architecture and method outperforms existing models
Max-Sliced Wasserstein Distance and its use for GANs
Generative adversarial nets (GANs) and variational auto-encoders have
significantly improved our distribution modeling capabilities, showing promise
for dataset augmentation, image-to-image translation and feature learning.
However, to model high-dimensional distributions, sequential training and
stacked architectures are common, increasing the number of tunable
hyper-parameters as well as the training time. Nonetheless, the sample
complexity of the distance metrics remains one of the factors affecting GAN
training. We first show that the recently proposed sliced Wasserstein distance
has compelling sample complexity properties when compared to the Wasserstein
distance. To further improve the sliced Wasserstein distance we then analyze
its `projection complexity' and develop the max-sliced Wasserstein distance
which enjoys compelling sample complexity while reducing projection complexity,
albeit necessitating a max estimation. We finally illustrate that the proposed
distance trains GANs on high-dimensional images up to a resolution of 256x256
easily.Comment: Accepted to CVPR 201
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