{"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/interpnet-neural-introspection-for","title":"InterpNET: Neural Introspection for Interpretable Deep Learning","arxiv_id":"1710.09511","date":"2017-10-26","proceeding":null,"authors":["Shane Barratt"],"abstract":"Humans are able to explain their reasoning. On the contrary, deep neural\nnetworks are not. This paper attempts to bridge this gap by introducing a new\nway to design interpretable neural networks for classification, inspired by\nphysiological evidence of the human visual system's inner-workings. This paper\nproposes a neural network design paradigm, termed InterpNET, which can be\ncombined with any existing classification architecture to generate natural\nlanguage explanations of the classifications. The success of the module relies\non the assumption that the network's computation and reasoning is represented\nin its internal layer activations. While in principle InterpNET could be\napplied to any existing classification architecture, it is evaluated via an\nimage classification and explanation task. Experiments on a CUB bird\nclassification and explanation dataset show qualitatively and quantitatively\nthat the model is able to generate high-quality explanations. While the current\nstate-of-the-art METEOR score on this dataset is 29.2, InterpNET achieves a\nmuch higher METEOR score of 37.9.","url_abs":"http://arxiv.org/abs/1710.09511v2","url_pdf":"http://arxiv.org/pdf/1710.09511v2.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":"interpnet-neural-introspection-for","repo_url":"https://github.com/sbarratt/interpnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}