mlsanityData to 7 Oct 2026

Swapping Autoencoder for Deep Image Manipulation

arXiv 2007.00653 ↗First posted 1 Jul 2020cs.CV cs.GR cs.LGcited by 215

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Cited by 30 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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 encode an image with two independent components and enforce that any swapped combination maps to a realistic image. In particular, we encourage the components to represent structure and texture, by enforcing one component to encode co-occurrent patch statistics across different parts of an image. As our method is trained with an encoder, finding the latent codes for a new input image becomes trivial, rather than cumbersome. As a result, it can be used to manipulate real input images in various ways, including texture swapping, local and global editing, and latent code vector arithmetic. Experiments on multiple datasets show that our model produces better results and is substantially more efficient compared to recent generative models.

Key facts. Cited by 215 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2020.

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Citing papers per year: 2021: 52; 2022: 71; 2023: 49; 2024: 25; 2025: 12; 2026: 6.

215 papers.

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50 most cited of 215 All 215 (JSON)