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In this paper, we propose a new algorithm, namely a manifold guided\ngenerative adversarial network (MGGAN), which leverages a guidance network on\nexisting GAN architecture to induce generator learning all modes of data\ndistribution. Based on extensive evaluations, we show that our algorithm\nresolves mode collapse without losing image quality. In particular, we\ndemonstrate that our algorithm is easily extendable to various existing GANs.\nExperimental analysis justifies that the proposed algorithm is an effective and\nefficient tool for training GANs.","url_abs":"http://arxiv.org/abs/1804.04391v1","url_pdf":"http://arxiv.org/pdf/1804.04391v1.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":"mggan-solving-mode-collapse-using-manifold","repo_url":"https://github.com/QuickSolverKyle/Tensorflow-MyGANs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.04391","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.04391"}},"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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