{"paper":{"title":"RES: A Robust Framework for Guiding Visual Explanation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangji Bai, Liang Zhao, Siyi Gu, Sungsoo Ray Hong, Tong Steven Sun, Yuyang Gao","submitted_at":"2022-06-27T16:06:27Z","abstract_excerpt":"Despite the fast progress of explanation techniques in modern Deep Neural Networks (DNNs) where the main focus is handling \"how to generate the explanations\", advanced research questions that examine the quality of the explanation itself (e.g., \"whether the explanations are accurate\") and improve the explanation quality (e.g., \"how to adjust the model to generate more accurate explanations when explanations are inaccurate\") are still relatively under-explored. To guide the model toward better explanations, techniques in explanation supervision - which add supervision signals on the model expla"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.13413","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2206.13413/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}