{"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/synthesizing-the-preferred-inputs-for-neurons","title":"Synthesizing the preferred inputs for neurons in neural networks via deep generator networks","arxiv_id":"1605.09304","date":"2016-05-30","proceeding":"NeurIPS 2016 12","authors":["Anh Nguyen","Alexey Dosovitskiy","Jason Yosinski","Thomas Brox","Jeff Clune"],"abstract":"Deep neural networks (DNNs) have demonstrated state-of-the-art results on\nmany pattern recognition tasks, especially vision classification problems.\nUnderstanding the inner workings of such computational brains is both\nfascinating basic science that is interesting in its own right - similar to why\nwe study the human brain - and will enable researchers to further improve DNNs.\nOne path to understanding how a neural network functions internally is to study\nwhat each of its neurons has learned to detect. One such method is called\nactivation maximization (AM), which synthesizes an input (e.g. an image) that\nhighly activates a neuron. Here we dramatically improve the qualitative state\nof the art of activation maximization by harnessing a powerful, learned prior:\na deep generator network (DGN). The algorithm (1) generates qualitatively\nstate-of-the-art synthetic images that look almost real, (2) reveals the\nfeatures learned by each neuron in an interpretable way, (3) generalizes well\nto new datasets and somewhat well to different network architectures without\nrequiring the prior to be relearned, and (4) can be considered as a\nhigh-quality generative method (in this case, by generating novel, creative,\ninteresting, recognizable images).","url_abs":"http://arxiv.org/abs/1605.09304v5","url_pdf":"http://arxiv.org/pdf/1605.09304v5.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":"synthesizing-the-preferred-inputs-for-neurons","repo_url":"https://github.com/AndyDodss/iicnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"caffe2","reach":null},{"paper_slug":"synthesizing-the-preferred-inputs-for-neurons","repo_url":"https://github.com/Evolving-AI-Lab/synthesizing","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"caffe2","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"synthesizing-the-preferred-inputs-for-neurons","repo_url":"https://github.com/KamitaniLab/cnnpref","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"caffe2","reach":null},{"paper_slug":"synthesizing-the-preferred-inputs-for-neurons","repo_url":"https://github.com/KamitaniLab/icnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"caffe2","reach":null},{"paper_slug":"synthesizing-the-preferred-inputs-for-neurons","repo_url":"https://github.com/ndey96/deep-generator-network","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1605.09304","atlas_url":"https://app.syntology.ai/?focus=1605.09304","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.09304"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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