{"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/innvestigate-neural-networks","title":"iNNvestigate neural networks!","arxiv_id":"1808.04260","date":"2018-08-13","proceeding":null,"authors":["Maximilian Alber","Sebastian Lapuschkin","Philipp Seegerer","Miriam Hägele","Kristof T. Schütt","Grégoire Montavon","Wojciech Samek","Klaus-Robert Müller","Sven Dähne","Pieter-Jan Kindermans"],"abstract":"In recent years, deep neural networks have revolutionized many application\ndomains of machine learning and are key components of many critical decision or\npredictive processes. Therefore, it is crucial that domain specialists can\nunderstand and analyze actions and pre- dictions, even of the most complex\nneural network architectures. Despite these arguments neural networks are often\ntreated as black boxes. In the attempt to alleviate this short- coming many\nanalysis methods were proposed, yet the lack of reference implementations often\nmakes a systematic comparison between the methods a major effort. The presented\nlibrary iNNvestigate addresses this by providing a common interface and\nout-of-the- box implementation for many analysis methods, including the\nreference implementation for PatternNet and PatternAttribution as well as for\nLRP-methods. To demonstrate the versatility of iNNvestigate, we provide an\nanalysis of image classifications for variety of state-of-the-art neural\nnetwork architectures.","url_abs":"http://arxiv.org/abs/1808.04260v1","url_pdf":"http://arxiv.org/pdf/1808.04260v1.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":"innvestigate-neural-networks","repo_url":"https://github.com/albermax/innvestigate","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"interpretable-machine-learning","task_name":"Interpretable Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.04260","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}