46 research outputs found
Bidirectional Conditional Generative Adversarial Networks
Conditional Generative Adversarial Networks (cGANs) are generative models
that can produce data samples () conditioned on both latent variables ()
and known auxiliary information (). We propose the Bidirectional cGAN
(BiCoGAN), which effectively disentangles and in the generation process
and provides an encoder that learns inverse mappings from to both and
, trained jointly with the generator and the discriminator. We present
crucial techniques for training BiCoGANs, which involve an extrinsic factor
loss along with an associated dynamically-tuned importance weight. As compared
to other encoder-based cGANs, BiCoGANs encode more accurately, and utilize
and more effectively and in a more disentangled way to generate
samples.Comment: To appear in Proceedings of ACCV 201
CapsuleGAN: Generative Adversarial Capsule Network
We present Generative Adversarial Capsule Network (CapsuleGAN), a framework
that uses capsule networks (CapsNets) instead of the standard convolutional
neural networks (CNNs) as discriminators within the generative adversarial
network (GAN) setting, while modeling image data. We provide guidelines for
designing CapsNet discriminators and the updated GAN objective function, which
incorporates the CapsNet margin loss, for training CapsuleGAN models. We show
that CapsuleGAN outperforms convolutional-GAN at modeling image data
distribution on MNIST and CIFAR-10 datasets, evaluated on the generative
adversarial metric and at semi-supervised image classification.Comment: To appear in Proceedings of ECCV Workshop on Brain Driven Computer
Vision (BDCV) 201