Browse State-of-the-Art › Style Transfer
Style Transfer
759 papers with code · 3 benchmarks · 20 datasets archive 2025-07-28
Style Transfer is a technique in computer vision and graphics that involves generating a new image by combining the content of one image with the style of another image. The goal of style transfer is to create an image that preserves the content of the original image while applying the visual style of another image.
( Image credit: A Neural Algorithm of Artistic Style )
- "T" as a sofa:
The "T" horizontal strip can mimic the back of a sofa with a delicate cushion or details of the uphols or appliances with the color button.
The "T" vertical strip can show a feet or arm of the sofa, shiny, yet firm.
- Merge "P":
Put "P" next to "T", your curve to delicately with the top "T." It is intertwined. The circular part of "P" can show a cushion or a curved chair and synchronize the subject of furniture.
Make sure "P" is visually relying on "T", which reflects the relationship of cohesion and balance.
- Coherence of "B" and "I":
"B" can be aligned as a pair of cushions or a modern chair, with mild curves with glossy and modern aesthetics.
"I" can be a symbol of a shiny furniture or a vertical light bar and completes the shapes without overburdess them.
Color palette 4:
Includes soft soil colors such as beige, top and gray shades, along with silent or silver gold tips to touch elegance.
Consider a slope effect to enhance modernity, to keep colors elegant and complex.
- Connect the letters:
Use the overlap or intertwined edges that the letters meet for the symbol of unity.
The plan should allow viewers to distinguish each letter while feeling part of the same "structure".
- Background patterns:
Use delicate geometric patterns or textures that mimic fabrics or furniture materials such as wood seeds or woven fibers.
These patterns must remain minimalist and focus on highlighting the logo, while maintaining communication.
While it deals with the subject of furniture and design, this concept conveys modernity, creativity and professional. If you like, I can create a draft design for better visualization.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
3 leaderboard tables shown for this task (2 more in the archive withheld as spam; see /not-shown), 3 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| StyleBench (7 rows) | StyleShot | StyleShot: A Snapshot on Any Style | code | Syntology ran 5 of 9 samples · 4 unverified | Compare |
| WikiArt (2 rows) | StyleFlow-Content-Fixed-I2I | StyleFlow For Content-Fixed Image to Image Translation | code | — | Compare |
| GYAFC (1 row) | BART (TextBox 2.0) | TextBox 2.0: A Text Generation Library with Pre-trained Language Models | code | Syntology ran 1 of 1 samples · 0 unverified | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
20 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
6 subtasks in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
30 shown of 759 papers with code (1,661 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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26 Aug 2015 284 repositories listed Syntology ran 42 of 110 samples · 68 unverified · 39 pointer-only (licence)In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image.
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30 Mar 2017 190 repositories listed Syntology ran 6 of 31 samples · 25 unverified · 6 pointer-only (licence)Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs.
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27 Mar 2016 80 repositories listed Syntology ran 13 of 46 samples · 33 unverified · 5 pointer-only (licence)We consider image transformation problems, where an input image is transformed into an output image.
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20 Mar 2017 29 repositories listed Syntology ran 28 of 41 samples · 13 unverified · 29 pointer-only (licence)Gatys et al.
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27 Jul 2016 22 repositories listed Syntology ran 1 of 18 samples · 17 unverifiedIt this paper we revisit the fast stylization method introduced in Ulyanov et.
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22 Mar 2017 21 repositories listed Syntology ran 9 of 24 samples · 15 unverified · 6 pointer-only (licence)This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style.
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18 May 2017 20 repositories listedIn this paper, we present a method which combines the flexibility of the neural algorithm of artistic style with the speed of fast style transfer networks to allow real-time stylization using any content/style image…
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23 May 2017 15 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)The whitening and coloring transforms reflect a direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural…
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2 Oct 2016 14 repositories listed Syntology ran 2 of 8 samples · 6 unverified · 5 pointer-only (licence)We present a novel method for constructing Variational Autoencoder (VAE).
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26 May 2017 12 repositories listed Syntology ran 6 of 7 samples · 1 unverified · 6 pointer-only (licence)We demonstrate the effectiveness of this cross-alignment method on three tasks: sentiment modification, decipherment of word substitution ciphers, and recovery of word order.
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14 May 2019 11 repositories listedOn the other hand, CVAE training is simple but does not come with the distribution-matching property of a GAN.
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23 Mar 2018 11 repositories listed Syntology ran 6 of 21 samples · 15 unverified · 7 pointer-only (licence)In this work, we propose "global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system.
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10 Mar 2016 10 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)Gatys et al.
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26 Jul 2018 9 repositories listedThese and our qualitative results ranging from small image patches to megapixel stylistic images and videos show that our approach better captures the subtle nature in which a style affects content.
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11 May 2017 9 repositories listedWe first propose a taxonomy of current algorithms in the field of NST.
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3 Jul 2018 8 repositories listedImage style transfer models based on convolutional neural networks usually suffer from high temporal inconsistency when applied to videos.
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19 Jun 2016 7 repositories listedThis note presents an extension to the neural artistic style transfer algorithm (Gatys et al.).
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5 Mar 2016 7 repositories listedConvolutional neural networks (CNNs) have proven highly effective at image synthesis and style transfer.
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20 Feb 2023 6 repositories listedRecent large-scale generative models learned on big data are capable of synthesizing incredible images yet suffer from limited controllability.
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23 Apr 2020 6 repositories listed Syntology ran 0 of 12 samples · 12 unverifiedSpeech information can be roughly decomposed into four components: language content, timbre, pitch, and rhythm.
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29 Apr 2019 6 repositories listed Syntology ran 0 of 11 samples · 11 unverifiedStyle transfer algorithms strive to render the content of one image using the style of another.
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17 Apr 2018 6 repositories listed Syntology ran 0 of 8 samples · 8 unverifiedWe consider the task of text attribute transfer: transforming a sentence to alter a specific attribute (e.
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1 Dec 2017 6 repositories listedIn this work, we focus on the challenge of taking partial observations of highly-stylized text and generalizing the observations to generate unobserved glyphs in the ornamented typeface.
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13 Jun 2017 6 repositories listed Syntology ran 2 of 2 samples · 0 unverifiedThis adversarially regularized autoencoder (ARAE) allows us to generate natural textual outputs as well as perform manipulations in the latent space to induce change in the output space.
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20 Mar 2017 6 repositories listed Syntology ran 1 of 14 samples · 13 unverifiedDespite the rapid progress in style transfer, existing approaches using feed-forward generative network for multi-style or arbitrary-style transfer are usually compromised of image quality and model flexibility.
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13 Dec 2016 6 repositories listed Syntology ran 0 of 5 samples · 5 unverifiedThis results in a procedure for artistic style transfer that is efficient but also allows arbitrary content and style images.
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23 Nov 2016 6 repositories listed Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)Neural Style Transfer has shown very exciting results enabling new forms of image manipulation.
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19 Oct 2020 5 repositories listedA crucial aspect for the successful deployment of audio-based models "in-the-wild" is the robustness to the transformations introduced by heterogeneous acquisition conditions.
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10 Feb 2020 5 repositories listed Syntology ran 18 of 25 samples · 7 unverified · 12 pointer-only (licence)Across all style transfer tasks, our approach yields substantial gains over state-of-the-art non-generative baselines, including the state-of-the-art unsupervised machine translation techniques that our approach…
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26 Oct 2019 5 repositories listedMellotron is a multispeaker voice synthesis model based on Tacotron 2 GST that can make a voice emote and sing without emotive or singing training data.
Syntology lines on 19 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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