Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourage convex latent distributions

We present a neural network architecture based upon the Autoencoder (AE) and Generative Adversarial Network (GAN) that promotes a convex latent distribution by training adversarially on latent space interpolations. By using an AE as both the generator and discriminator of a GAN, we pass a pixel-wise error function across the discriminator, yielding an AE which produces non-blurry samples that match both high- and low-level features of the original images... (read more)

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METHOD TYPE
AE
Dimensionality Reduction
Convolution
Convolutions
AutoEncoder
Generative Models
GAN
Generative Models