{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sketchygan-towards-diverse-and-realistic","title":"SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis","arxiv_id":"1801.02753","date":"2018-01-09","proceeding":"CVPR 2018 6","authors":["Wengling Chen","James Hays"],"abstract":"Synthesizing realistic images from human drawn sketches is a challenging\nproblem in computer graphics and vision. Existing approaches either need exact\nedge maps, or rely on retrieval of existing photographs. In this work, we\npropose a novel Generative Adversarial Network (GAN) approach that synthesizes\nplausible images from 50 categories including motorcycles, horses and couches.\nWe demonstrate a data augmentation technique for sketches which is fully\nautomatic, and we show that the augmented data is helpful to our task. We\nintroduce a new network building block suitable for both the generator and\ndiscriminator which improves the information flow by injecting the input image\nat multiple scales. Compared to state-of-the-art image translation methods, our\napproach generates more realistic images and achieves significantly higher\nInception Scores.","url_abs":"http://arxiv.org/abs/1801.02753v2","url_pdf":"http://arxiv.org/pdf/1801.02753v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sketchygan-towards-diverse-and-realistic","repo_url":"https://github.com/wchen342/SketchyGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.02753","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1801.02753"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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