mlsanityData to 7 Oct 2026

From A to Z: Supervised Transfer of Style and Content Using Deep Neural Network Generators

arXiv 1603.02003 ↗First posted 7 Mar 2016cs.CVcited by 14

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Abstract (arXiv)

We propose a new neural network architecture for solving single-image analogies - the generation of an entire set of stylistically similar images from just a single input image. Solving this problem requires separating image style from content. Our network is a modified variational autoencoder (VAE) that supports supervised training of single-image analogies and in-network evaluation of outputs with a structured similarity objective that captures pixel covariances. On the challenging task of generating a 62-letter font from a single example letter we produce images with 22.4% lower dissimilarity to the ground truth than state-of-the-art.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Mar 2016.

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Citing papers per year: 2021: 7; 2022: 4; 2023: 1; 2024: 1; 2025: 0; 2026: 1.

14 papers.

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