Style Transfer: A Decade Survey

Hao Tang; Tianshan Zhang

REVIEW 5 major objections 6 minor 298 references

Style Transfer: A Decade Survey

T0 review · 5 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper claims to supply a single map of a decade of style transfer — more than 500 papers organized around VAE, GAN, and diffusion models — together with a multidimensional evaluation framework for comparing technical and artistic…

desk verdict A broad but unreliable survey; the citation errors and missing table symbols break the trust a reference work needs. read the letter →

arxiv 2506.19278 v1 pith:NAQKMNJ5 submitted 2025-06-24 cs.GR

Tianshan Zhang , Hao Tang This is my paper

classification cs.GR MSC 68T0768U10

keywords styletransfergenerativemodelsvariationalautoencodersadversarialnetworksdiffusionautoregressiveevaluationframeworkAIGC

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Over the past decade, style transfer — turning the content of one image into the visual style of another — has moved from slow per-image optimisation to millisecond generators and, most recently, diffusion pipelines. This survey claims to give the field a single map: it organises more than 500 papers from roughly 2015 to 2025 into three dominant generative families — variational autoencoders, generative adversarial networks, and diffusion models, with flow matching and autoregressive generation as further threads — and reads the whole history as a series of trades between perceptual fidelity, speed, and output diversity. It also proposes a multidimensional evaluation framework covering artistic merit, visual quality, computational efficiency, robustness, multimodal capability, and ethical and safety concerns, which the field currently lacks as a common yardstick. If the map is right, a reader gets a historical narrative, a comparison table for choosing among 2023-2025 methods, and a clear statement of the open problem: combining diffusion-level quality with autoregressive structural control.

What carries the argument

The argument is carried by a taxonomy: every surveyed method is classified as VAE-, GAN-, diffusion-, flow-matching-, or autoregressive-based, and each family is analysed through the same question of how it disentangles content from style. The technical anchor is the representation of style as second-order statistics of deep features, the Gram matrix $G_\ell(I) = \Phi_\ell(I)\Phi_\ell(I)^\top$, which the early neural-style-transfer work introduced, later replaced by the cheaper per-channel moments of Adaptive Instance Normalization, whose affine transform $\sigma(x_s)(x_c-\mu(x_c))/\sigma(x_c) + \mu(x_s)$ is equation (2) of the survey. The survey's own proposed instrument is the multidimensional evaluation framework: a named set of axes intended to serve as a common comparison standard for methods, implemented concretely in Table 6, which benchmarks 2023-2025 methods along multimodal capability, artistic merit, visual quality, computational efficiency, and robustness. These three pieces — the taxonomy, the style-statistics anchor, and the evaluation rubric — together carry the survey's claim to be a unified perspective on the field.

What would settle it

Spot-check the survey's tables against its reference list: look up whether reference [9] is 'Generating long sequences with sparse transformers' while Table 2 calls it 'BigVAE', whether reference [48] is the SPADE paper while Table 1 credits it to SEAN, and whether Table 6 contains any legend that explains its rating symbols. If a substantial fraction of such checks fails to resolve, then the survey's benchmark apparatus is not usable even though its high-level historical narrative may still be sound.

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Extended reading notes

Core claim

On the paper's own terms, a decade of style-transfer research is explained by the successive dominance of three generative paradigms: VAEs, which provide structured latent spaces for content–style separation; GANs, which brought photorealistic fidelity and real-time inference; and diffusion models, which exchange speed for high-fidelity, text-controllable stylisation at 4K scale. The survey frames the entire field as the search for a map $F: (I_c, I_s) \mapsto I_t$ that preserves the content image's structure while matching the style image's statistics, and it treats the Gram-matrix formulation of style and its cheaper AdaIN replacement as the field's conceptual spine. Its own contribution is the multidimensional evaluation framework — in the abstract stated as Technical Innovation, Artistic Merit, Visual Quality, Computational Efficiency, and Creative Potential, and in the body expanded into perceptual, content-preservation, diversity, quality, alignment, and practical axes — together with a timeline, method tables, an application survey across portraits, video, 3D, text, and domain adaptation, and a collation of datasets and metrics. The survey concludes that GANs remain the choice for low-latency deployment, diffusion models lead in quality and controllability but risk content leakage, and hybrid autoregressive–diffusion frameworks are the most promising direction.

Load-bearing premise

The survey's value as a reference map rests on the accuracy of its compiled citations and benchmark ratings, and this premise is already giving way in visible places — a table row labels the Sparse Transformer paper as 'BigVAE', SEAN is credited to the SPADE paper's reference number, and the benchmark table's rating symbols are missing, so the central comparisons cannot currently be trusted.

Editorial extensions

If this is right

A newcomer to the field gets a structured reading order: VAE work for latent-space control, GAN work for fast photoreal transfer, and diffusion work for text-guided high-fidelity stylisation.
The proposed evaluation axes give researchers a shared vocabulary for reporting results, so methods can be compared on efficiency and artistic merit rather than on pixel metrics alone.
The survey's benchmark of 2023-2025 methods, once its rating table is usable, would let practitioners pick a method by the axis they care about — speed, fidelity, control, or robustness.
The identified weakness of diffusion models — content leakage and difficulty of localised edits — combined with the layout fidelity of autoregressive models, points to hybrid AR-diffusion pipelines as the field's next milestone.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the evaluation rubric were adopted as a community standard, a compact per-method profile over its axes could replace the current practice of reporting only FID and LPIPS, and would make cross-paper comparisons meaningful.
The survey's visible attribution errors suggest the high-level narrative — from GANs to diffusion — is robust, but the fine-grained placement of individual methods needs a validation pass; an automated citation-consistency check across all 500 references would be a cheap way to test this.
The ethical axis the survey introduces implies that style-transfer evaluation is drifting from low-level image metrics toward authorship and cultural-sensitivity questions, which would in turn affect how datasets such as Danbooru may legitimately be used in training.
A testable extension of the survey's hybrid-AR-diffusion recommendation is to measure whether injecting flow-matching ODE solvers into autoregressive decoding preserves the layout fidelity of AR while gaining diffusion-style quality; the survey names this as a direction but does not quantify it.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Dear colleague,

I read the style transfer survey. I agree with the reader: this is a reject, and the reason is load-bearing citation errors, not the absence of new results. The paper does have useful parts: the 2023-2025 coverage of diffusion and autoregressive methods is genuinely fresh, and the Figure 1 timeline is a decent quick map. The mathematical fundamentals (Gram loss, AdaIN, flow matching, diffusion) are correct, and the broad narrative from GANs to diffusion is standard but well written.

What sinks it is reliability. A survey's job is to give the reader a map they can trust, and the map has broken pointers in visible places. Table 2 cites 'BigVAE [9]' when reference [9] is the Sparse Transformer paper. Table 1 attributes SEAN to reference [48], which is SPADE. Table 6's caption defines rating symbols that never render, so the benchmark ratings are unreadable. Table 4 uses undefined acronyms. These are not isolated typos; they appear in the core tables that the survey's value depends on.

There is also no literature selection methodology, so we cannot audit the claim of 500 papers, and the proposed evaluation framework is a taxonomy, not a validated tool. The heavy self-citation is not by itself a flaw, but combined with the missing methodology it makes coverage hard to trust. The paper also does not position itself against prior style transfer surveys, which is a missed opportunity.

If the authors fixed the reference tables and added a selection protocol, this could become a useful reference for practitioners who want a fast overview of 2023-2025 work. As it stands, I would desk reject and invite resubmission after a thorough correction pass.

Best

Referee Report

5 major / 6 minor

Summary. This manuscript is a survey of AI-driven style transfer and generative modeling, spanning neural style transfer foundations, VAE/GAN/diffusion/autoregressive paradigms, applications (portrait, video, 3D, text, domain adaptation), datasets, and evaluation metrics. The abstract claims a systematic review of over 500 papers and proposes a multidimensional evaluation framework. The paper is organized as a conventional survey with background equations, timelines, method tables, and an appendix containing expanded discussions and further tables.

Significance. If the reference backbone and tables were accurate, this would be a useful entry point for researchers entering style transfer: the high-level narrative is standard, the coverage is broad, and the timeline and basic formulas provide a helpful orientation. The paper does not provide machine-checked proofs, code, or quantitative benchmarks; its value rests entirely on the trustworthiness of its citations and survey tables. That trustworthiness fails in several visible, checkable places, so the central claim of a comprehensive and reliable survey is not currently supported. I agree with the stress-test concern that citation accuracy is load-bearing for a survey of this kind.

major comments (5)

[Table 2 / §3.1] Table 2 lists 'BigVAE [9]' as a 2018 ICLR large-scale VAE, but reference [9] is Child et al., 'Generating Long Sequences with Sparse Transformers' (ICLR 2019). This is not an isolated case: Table 8 attributes 'WikiArt' to reference [413] (Mancini et al.) and 'Stylized ImageNet' to reference [412] (Geirhos et al.), and Section 10.2.4 cites 'SANet [500]' where reference [500] is a visual tracking paper, not the style-transfer SANet. Because readers must be able to trust that table entries correspond to the cited works, such mismatches in core tables are a load-bearing reliability failure.
[§2.3, Table 1, §10.2.2] SEAN is cited as [48] in Section 2.3, Table 1, and Section 10.2.2, but reference [48] is Zhu et al., 'Semantic Image Synthesis with Spatially-Adaptive Normalization' (SPADE, CVPR 2020). The actual SEAN paper is Park et al., ECCV 2020. The same wrong citation recurs in three places, so it cannot be dismissed as a single typo, and it directly affects the paper's usability as a reliable reference.
[Abstract / §4.2 / Table 6] The claimed multidimensional evaluation framework is not delivered. The abstract promises five dimensions including Technical Innovation and Creative Potential, but Section 4.2 defines six axes (AM, VQ, CE, R, MC, ES) without those two. Table 6's caption defines three rating symbols ('Excellent', 'Generally Good', 'Relatively Low Computational Efficiency'), but the first and third symbols do not render in the table body, and several rows contain no symbols at all, so the benchmark ratings are uninterpretable. Appendix 10.2 gives narrative discussion rather than measurement protocols, so the framework is not operationalized.
[Table 4 / §3.3] Table 4 is unreadable as a technical guide because it introduces undefined acronyms such as SBGMLS, SBGM-SDE, SBGM-CDLD, GM-EGDD, PFGM, VP-DGM-SM, HRIS-LDM, Frido-FPD, VQDM-TIS, CMCDM, CCDF-SC, PNM-DMM, SDDMDSS, and ASM-ISIG without expanding them or mapping them to method names. In addition, several bracketed references do not correspond to the named acronym; for example, 'CMCDM [243]' points to a context-prediction paper (Yang et al., NeurIPS 2023), not a conditional-modality diffusion model. This table is a core component of the diffusion review and needs a complete rewrite.
[Abstract / §1] The paper states that it 'systematically review[s] over 500 research papers,' but no methodology is provided for the literature selection: there is no description of search databases, inclusion/exclusion criteria, time-span restrictions, or a protocol for handling duplicate and retracted works. The claimed 500-paper count is not reconciled with the reference list. Without a stated methodology, the coverage claim cannot be audited, which is especially problematic given the citation errors documented above.

minor comments (6)

[§11.2] The heading 'Video Style Tranfer' contains a typo, and the author affiliation line writes 'T ang' for 'Tang'; please correct these and scan the text for similar typos.
[§2.2, Eq. (2)] As rendered, the AdaIN formula lacks explicit parentheses around the standardized content features, making the expression ambiguous; please check the typesetting.
[Figure 1] The timeline lists 'ControlNet[29]' and 'ControlNet[31]' as separate entries and uses the unexplained label 'Inst[30]'; reference [29] is not the ControlNet paper, so the timeline needs correction.
[References] The reference list contains duplicate entries, e.g., CLIP as [17] and [43], Latent Diffusion Models as [22] and [194], and DDIM as [13] and [209]; these should be merged to avoid confusion.
[Table 4 / Table 5] Table 4's caption calls the table 'This figure,' and Table 5 labels reference [257] as 'PixelCNN-HF' even though Section 3.4 and the reference list identify [257] as PixelCNN++; please make labels and captions consistent.
[§10.1.2 / Table 3] Section 10.1.2 introduces CLIP Score as a content-retention metric, but reference [486] is an image-captioning metric; please clarify the usage or replace the citation. In addition, Table 3's 'FID-GAN [130]' row cites the FID metric paper rather than a GAN architecture.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation chain: the survey's coverage and evaluation claims are descriptive aggregations, not derivations from fitted inputs; the visible citation and table defects are reliability errors, not circular reductions.

full rationale

This is a literature survey with no prediction, fitted parameter, uniqueness theorem, or first-principles derivation. The central claim—'we systematically review over 500 research papers' and 'propose a multidimensional evaluation framework'—is an organizational description, not a result obtained from equations or from the authors' prior work. The evaluation framework in Section 4 defines dimensions such as Artistic Merit, Visual Quality, and Computational Efficiency by prose and then applies subjective symbols in Table 6; nothing in that construction forces the benchmark outcomes, so the framework is not self-definitional. The paper does cite many works by author Hao Tang (DF-GAN [20], GALIP [25], LART [315], HART [187], SCP-Diff [242], SSR-Encoder [293], and others), but none of those citations is the sole support for the survey's central premise, and the survey does not invoke them to forbid alternatives or to define its taxonomy. I also considered the visible reliability failures: Table 2 lists 'BigVAE [9]' where reference [9] is Child et al., 'Generating Long Sequences with Sparse Transformers', not a large-scale VAE; Table 1 attributes 'SEAN [48]' to reference [48], which is the SPADE paper; and Table 6's rating symbols are defined in the caption but absent from the table body. These are significant missing-support and citation-accuracy defects for a survey whose value depends on reference reliability, and they should be reported as correctness and quality risks; however, they are not cases where a claim is equivalent to its input by construction. Accordingly, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The survey's central claims rest on the reliability of its compiled references and the representativeness of its selection; no novel derivations or fitted parameters are involved.

assumptions (3)

domain assumption The organization of the field into VAE, GAN, diffusion, and autoregressive families is complete and mutually exclusive.
Section 1.1 and Section 3 present this taxonomy as the organizing structure for the survey without justifying completeness or exclusivity.
domain assumption The evaluation axes proposed in Section 4 (Technical Innovation, Artistic Merit, Visual Quality, Computational Efficiency, Creative Potential) are valid and sufficient for assessing style transfer systems.
The axes are stated normatively in Section 4 and Appendix 10, but no empirical validation or comparison to existing metrics is provided.
standard math The equations for the Gram matrix, AdaIN, diffusion forward/reverse process, and flow matching are standard and correct.
Used in Section 2.1, 2.5, 2.6, 2.8; these are textbook-level definitions, though some are sketched and, in the case of Eq. (8), the flow-matching target is not the derivative of the stated interpolation path.

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Cite this review

Pith. "Pith review of Style Transfer: A Decade Survey." pith.science (2026). https://pith.science/paper/NAQKMNJ5

@misc{pith2026250619278,
  author       = {Pith},
  title        = {Pith review of: Style Transfer: A Decade Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NAQKMNJ5}},
  note         = {Machine review of arXiv:2506.19278}
}

read the original abstract

The revolutionary advancement of Artificial Intelligence Generated Content (AIGC) has fundamentally transformed the landscape of visual content creation and artistic expression. While remarkable progress has been made in image generation and style transfer, the underlying mechanisms and aesthetic implications of these technologies remain insufficiently understood. This paper presents a comprehensive survey of AIGC technologies in visual arts, tracing their evolution from early algorithmic frameworks to contemporary deep generative models. We identify three pivotal paradigms: Variational Autoencoders (VAE), Generative Adversarial Networks (GANs), and Diffusion Models, and examine their roles in bridging the gap between human creativity and machine synthesis. To support our analysis, we systematically review over 500 research papers published in the past decade, spanning both foundational developments and state-of-the-art innovations. Furthermore, we propose a multidimensional evaluation framework that incorporates Technical Innovation, Artistic Merit, Visual Quality, Computational Efficiency, and Creative Potential. Our findings reveal both the transformative capacities and current limitations of AIGC systems, emphasizing their profound impact on the future of creative practices. Through this extensive synthesis, we offer a unified perspective on the convergence of artificial intelligence and artistic expression, while outlining key challenges and promising directions for future research in this rapidly evolving field.

Figures

Figures reproduced from arXiv: 2506.19278 by the authors.

**Figure 2.** Neural-style-transfer result reproduced from Gatys [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Given any two unordered sets of images X and Y, CycleGAN [ [PITH_FULL_IMAGE:figures/full_fig_p023_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: The condition LDM [22] either via concatenation or by a more general cross-attention mechanism. Latent Embedding Techniques: Latent embedding strategies have further bridged semantic representations and visual outcomes. CLIP-guided embeddings [17] map high-level desc…

**Figure 5.** Figure 5: The Diffusion Transformer (DiT) architecture. [ [PITH_FULL_IMAGE:figures/full_fig_p025_5.png]

**Figure 6.** Figure 6: The training and inference paradigm of DEADiff [PITH_FULL_IMAGE:figures/full_fig_p025_6.png]

**Figure 7.** Figure 7: Overview of the Art-Free framework [267]. The model consists of three main components: (left) Art-Free SAM trained on a large dataset of real-world images without artistic content, (middle) an Art-Free Diffusion model that processes text prompts and generates images, a…

**Figure 8.** Figure 8: The figure illustrates a diffusion-based framework for gesture generation, as proposed in DiffuseStyleGesture [ [PITH_FULL_IMAGE:figures/full_fig_p031_8.png]

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Reference graph

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Z-star+: A zero-shot style transfer method via adjusting style distribution,

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Artadapter: Text-to- image style transfer using multi-level style encoder and explicit adaptation,

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APDrawingGAN: Generating artistic portrait drawings from face photos with hier- archical gans,

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Warpgan: Automatic caricature generation,

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Fine-tuning stylegan2 for cartoon face generation,

J. Back, “Fine-tuning stylegan2 for cartoon face generation,” 2021

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Dynagan: Dynamic few-shot adaptation of gans to multiple domains,

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Image-based clip- guided essence transfer,

H. Chefer, S. Benaim, R. Paiss, and L. Wolf, “Image-based clip- guided essence transfer,” arXiv: 2110.12427, 2021

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Dct-net: Domain- calibrated translation for portrait stylization,

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Making robots draw a vivid portrait in two minutes,

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Spatchgan: A statistical feature based discriminator for unsupervised image-to-image translation,

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Stylecarigan: caricature generation via stylegan feature map modulation,

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Mind the gap: Domain gap control for single shot domain adaptation for gener- ative adversarial networks,

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2021
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Creativesynth: Creative blending and synthesis 17 of visual arts based on multimodal diffusion,

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General image-to-image translation with one-shot image guidance,

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Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models,

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Ssr-encoder: Encoding selective subject representation for subject-driven generation,

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Deformable one-shot face stylization via dino semantic guidance,

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Instantstyle-plus: Style transfer with content-preserving in text- to-image generation,

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Zepo: Zero-shot portrait stylization with faster sampling,

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Cus- tomizing text-to-image models with a single image pair,

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Reconet: Real-time coherent video style transfer network,

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Learning linear trans- formations for fast image and video style transfer,

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Vtoonify: Controllable high-resolution portrait video style transfer,

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2022

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

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Generating long sequences with sparse transformers,

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Semantic image synthesis with spatially-adaptive normalization,

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2020

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Improving diffusion-based image synthesis with context prediction,

L. Yang, J. Liu, S. Hong, Z. Zhang, Z. Huang, Z. Cai, W. Zhang, and B. CUI, “Improving diffusion-based image synthesis with context prediction,” in NeurIPS, 2023

work page 2023

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A neural algorithm of artistic style,

L. A. Gatys, “A neural algorithm of artistic style,” arXiv:1508.06576, 2015

arXiv 2015

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Auto-encoding variational bayes,

D. P . Kingma, “Auto-encoding variational bayes,” arXiv:1312.6114, 2013

arXiv 2013

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Generative adver- sarial nets,

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde- Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adver- sarial nets,” NeurIPS, 2014

2014

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Perceptual losses for real- time style transfer and super-resolution,

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2016

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Pixel recurrent neural networks,

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2016