{"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/deep-adversarial-neural-decoding","title":"Deep adversarial neural decoding","arxiv_id":"1705.07109","date":"2017-05-19","proceeding":null,"authors":["Yağmur Güçlütürk","Umut Güçlü","Katja Seeliger","Sander Bosch","Rob Van Lier","Marcel van Gerven"],"abstract":"Here, we present a novel approach to solve the problem of reconstructing\nperceived stimuli from brain responses by combining probabilistic inference\nwith deep learning. Our approach first inverts the linear transformation from\nlatent features to brain responses with maximum a posteriori estimation and\nthen inverts the nonlinear transformation from perceived stimuli to latent\nfeatures with adversarial training of convolutional neural networks. We test\nour approach with a functional magnetic resonance imaging experiment and show\nthat it can generate state-of-the-art reconstructions of perceived faces from\nbrain activations.","url_abs":"http://arxiv.org/abs/1705.07109v3","url_pdf":"http://arxiv.org/pdf/1705.07109v3.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":"deep-adversarial-neural-decoding","repo_url":"https://github.com/darkknight314/Deep-adversarial-neural-decoding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}