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To help answer this question, we design and\ninvestigate different image generation models associated with different loss\nfunctions to boost creativity in fashion generation. The dimensions of our\nexplorations include: (i) different Generative Adversarial Networks\narchitectures that start from noise vectors to generate fashion items, (ii)\nnovel loss functions that encourage novelty, inspired from Sharma-Mittal\ndivergence, a generalized mutual information measure for the widely used\nrelative entropies such as Kullback-Leibler, and (iii) a generation process\nfollowing the key elements of fashion design (disentangling shape and texture\ncomponents). A key challenge of this study is the evaluation of generated\ndesigns and the retrieval of best ones, hence we put together an evaluation\nprotocol associating automatic metrics and human experimental studies that we\nhope will help ease future research. We show that our proposed creativity\ncriterion yield better overall appreciation than the one employed in Creative\nAdversarial Networks. In the end, about 61% of our images are thought to be\ncreated by human designers rather than by a computer while also being\nconsidered original per our human subject experiments, and our proposed loss\nscores the highest compared to existing losses in both novelty and likability.","url_abs":"http://arxiv.org/abs/1804.00921v2","url_pdf":"http://arxiv.org/pdf/1804.00921v2.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":"design-design-inspiration-from-generative","repo_url":"https://github.com/blufzzz/Design-Inspiration-from-Generative-Networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.00921","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.00921"}},"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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