{"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/understanding-intermediate-layers-using","title":"Understanding intermediate layers using linear classifier probes","arxiv_id":"1610.01644","date":"2016-10-05","proceeding":null,"authors":["Guillaume Alain","Yoshua Bengio"],"abstract":"Neural network models have a reputation for being black boxes. We propose to\nmonitor the features at every layer of a model and measure how suitable they\nare for classification. We use linear classifiers, which we refer to as\n\"probes\", trained entirely independently of the model itself.\n  This helps us better understand the roles and dynamics of the intermediate\nlayers. We demonstrate how this can be used to develop a better intuition about\nmodels and to diagnose potential problems.\n  We apply this technique to the popular models Inception v3 and Resnet-50.\nAmong other things, we observe experimentally that the linear separability of\nfeatures increase monotonically along the depth of the model.","url_abs":"http://arxiv.org/abs/1610.01644v4","url_pdf":"http://arxiv.org/pdf/1610.01644v4.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":"understanding-intermediate-layers-using","repo_url":"https://github.com/ceegeechow/ECE471","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1610.01644","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}