{"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/inverting-the-generator-of-a-generative-1","title":"Inverting The Generator Of A Generative Adversarial Network (II)","arxiv_id":"1802.05701","date":"2018-02-15","proceeding":null,"authors":["Antonia Creswell","Anil A. Bharath"],"abstract":"Generative adversarial networks (GANs) learn a deep generative model that is\nable to synthesise novel, high-dimensional data samples. New data samples are\nsynthesised by passing latent samples, drawn from a chosen prior distribution,\nthrough the generative model. Once trained, the latent space exhibits\ninteresting properties, that may be useful for down stream tasks such as\nclassification or retrieval. Unfortunately, GANs do not offer an \"inverse\nmodel\", a mapping from data space back to latent space, making it difficult to\ninfer a latent representation for a given data sample. In this paper, we\nintroduce a technique, inversion, to project data samples, specifically images,\nto the latent space using a pre-trained GAN. Using our proposed inversion\ntechnique, we are able to identify which attributes of a dataset a trained GAN\nis able to model and quantify GAN performance, based on a reconstruction loss.\nWe demonstrate how our proposed inversion technique may be used to\nquantitatively compare performance of various GAN models trained on three image\ndatasets. We provide code for all of our experiments,\nhttps://github.com/ToniCreswell/InvertingGAN.","url_abs":"http://arxiv.org/abs/1802.05701v1","url_pdf":"http://arxiv.org/pdf/1802.05701v1.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":"inverting-the-generator-of-a-generative-1","repo_url":"https://github.com/ToniCreswell/InvertingGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.05701","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}