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Different from the recent mehtods, which directly synthesize the image from conditional input or look for meaningful editing operations in the existing latent ...

We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling. The key idea is to encode an ...

01.07.2020 · Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing images remains challenging. We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling. The key idea is to …

Abstract; Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing images remains challenging. We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling.

01.07.2020 · Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing images remains challenging. We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling.

Papertalk is an open-source platform where scientists share video presentations about their newest scientific results - and watch, like + discuss them.

We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling. The key idea is to encode an image ...

01.07.2020 · manipulation of existing images remains challenging. We propose the Swapping. Autoencoder, a deep model designed specifically for image manipulation, rather. than random sampling. The key idea is ...

Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei A. Efros, Richard Zhang. 16 Oct 2020NeurIPS 2020Readers: Everyone ...

The Swapping Autoencoder is proposed, a deep model designed specifically for image manipulation, rather than random sampling, ...

Abstract. Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing images remains challenging. We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling.

Swapping Autoencoder consists of autoencoding (top) and swapping (bottom) operation. Top: An encoder E embeds an input (Notre-Dame) into two codes. The ...

Swapping Autoencoder consists of autoencoding (top) and swapping (bottom) operation. Top: An encoder E embeds an input (Notre-Dame) into two ...

We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling.