Adversarial Latent Autoencoders
Introducing the Adversarial Latent Autoencoder (ALAE), a general architecture that can leverage recent improvements on GAN training procedures.
autoencoders generative-adversarial-networks latent-space disentanglement image-generation computer-vision code paper tutorial research arxiv:2004.04467

We designed two autoencoders: one based on a MLP encoder, and another based on a StyleGAN generator, which we call StyleALAE. We verify the disentanglement properties of both architectures. We show that StyleALAE can not only generate 1024x1024 face images with comparable quality of StyleGAN, but at the same resolution can also produce face reconstructions and manipulations based on real images.

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PhD student/Research Assistant at WVU Computer Vision, ML/AI, Computer graphics. Interned at AWS Rekognition. Ex gamedev, worked at Gameloft
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