179 research outputs found
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
LoGANv2: Conditional Style-Based Logo Generation with Generative Adversarial Networks
Domains such as logo synthesis, in which the data has a high degree of
multi-modality, still pose a challenge for generative adversarial networks
(GANs). Recent research shows that progressive training (ProGAN) and mapping
network extensions (StyleGAN) enable both increased training stability for
higher dimensional problems and better feature separation within the embedded
latent space. However, these architectures leave limited control over shaping
the output of the network, which is an undesirable trait in the case of logo
synthesis. This paper explores a conditional extension to the StyleGAN
architecture with the aim of firstly, improving on the low resolution results
of previous research and, secondly, increasing the controllability of the
output through the use of synthetic class-conditions. Furthermore, methods of
extracting such class conditions are explored with a focus on the human
interpretability, where the challenge lies in the fact that, by nature, visual
logo characteristics are hard to define. The introduced conditional style-based
generator architecture is trained on the extracted class-conditions in two
experiments and studied relative to the performance of an unconditional model.
Results show that, whilst the unconditional model more closely matches the
training distribution, high quality conditions enabled the embedding of finer
details onto the latent space, leading to more diverse output.Comment: accepted for poster presentation at ICMLA 2019, data+code available:
https://github.com/cedricoeldorf/ConditionalStyleGA
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
Diverse Image Generation via Self-Conditioned GANs
We introduce a simple but effective unsupervised method for generating
realistic and diverse images. We train a class-conditional GAN model without
using manually annotated class labels. Instead, our model is conditional on
labels automatically derived from clustering in the discriminator's feature
space. Our clustering step automatically discovers diverse modes, and
explicitly requires the generator to cover them. Experiments on standard mode
collapse benchmarks show that our method outperforms several competing methods
when addressing mode collapse. Our method also performs well on large-scale
datasets such as ImageNet and Places365, improving both image diversity and
standard quality metrics, compared to previous methods.Comment: CVPR 2020. Code: https://github.com/stevliu/self-conditioned-gan.
Webpage: http://selfcondgan.csail.mit.edu
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