4,104 research outputs found
Improving GAN with neighbors embedding and gradient matching
We propose two new techniques for training Generative Adversarial Networks
(GANs). Our objectives are to alleviate mode collapse in GAN and improve the
quality of the generated samples. First, we propose neighbor embedding, a
manifold learning-based regularization to explicitly retain local structures of
latent samples in the generated samples. This prevents generator from producing
nearly identical data samples from different latent samples, and reduces mode
collapse. We propose an inverse t-SNE regularizer to achieve this. Second, we
propose a new technique, gradient matching, to align the distributions of the
generated samples and the real samples. As it is challenging to work with
high-dimensional sample distributions, we propose to align these distributions
through the scalar discriminator scores. We constrain the difference between
the discriminator scores of the real samples and generated ones. We further
constrain the difference between the gradients of these discriminator scores.
We derive these constraints from Taylor approximations of the discriminator
function. We perform experiments to demonstrate that our proposed techniques
are computationally simple and easy to be incorporated in existing systems.
When Gradient matching and Neighbour embedding are applied together, our GN-GAN
achieves outstanding results on 1D/2D synthetic, CIFAR-10 and STL-10 datasets,
e.g. FID score of for the STL-10 dataset. Our code is available at:
https://github.com/tntrung/ganComment: Published as a conference paper at AAAI 201
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