1 research outputs found
Virtual Conditional Generative Adversarial Networks
When trained on multimodal image datasets, normal Generative Adversarial
Networks (GANs) are usually outperformed by class-conditional GANs and ensemble
GANs, but conditional GANs is restricted to labeled datasets and ensemble GANs
lack efficiency. We propose a novel GAN variant called virtual conditional GAN
(vcGAN) which is not only an ensemble GAN with multiple generative paths while
adding almost zero network parameters, but also a conditional GAN that can be
trained on unlabeled datasets without explicit clustering steps or objectives
other than the adversary loss. Inside the vcGAN's generator, a learnable
``analog-to-digital converter (ADC)" module maps a slice of the inputted
multivariate Gaussian noise to discrete/digital noise (virtual label),
according to which a selector selects the corresponding generative path to
produce the sample. All the generative paths share the same decoder network
while in each path the decoder network is fed with a concatenation of a
different pre-computed amplified one-hot vector and the inputted Gaussian
noise. We conducted a lot of experiments on several balanced/imbalanced image
datasets to demonstrate that vcGAN converges faster and achieves improved
Frech\'et Inception Distance (FID). In addition, we show the training byproduct
that the ADC in vcGAN learned the categorical probability of each mode and that
each generative path generates samples of specific mode, which enables
class-conditional sampling. Codes are available at
\url{https://github.com/annonnymmouss/vcgan