{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:BKNIXIKEOEQPS7TV2DA25S635M","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c6f6267cd2b70914f6018a2479afb24dc3fe836c857e5f9012fd021ce93ab937","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-12-20T16:45:54Z","title_canon_sha256":"6e02324c737aee549d89bbebe4922ce8aa15033116a178446188de2a46d320e7"},"schema_version":"1.0","source":{"id":"1312.6034","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1312.6034","created_at":"2026-07-04T18:53:55Z"},{"alias_kind":"arxiv_version","alias_value":"1312.6034v2","created_at":"2026-07-04T18:53:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1312.6034","created_at":"2026-07-04T18:53:55Z"},{"alias_kind":"pith_short_12","alias_value":"BKNIXIKEOEQP","created_at":"2026-07-04T18:53:55Z"},{"alias_kind":"pith_short_16","alias_value":"BKNIXIKEOEQPS7TV","created_at":"2026-07-04T18:53:55Z"},{"alias_kind":"pith_short_8","alias_value":"BKNIXIKE","created_at":"2026-07-04T18:53:55Z"}],"graph_snapshots":[{"event_id":"sha256:563d0ad72b91da6b2c12d783fa3d7623d6023bf4cc4a8045bb37de1ea53f46d5","target":"graph","created_at":"2026-07-04T18:53:55Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"We show that such maps can be employed for weakly supervised object segmentation using classification ConvNets."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the gradient of the class score with respect to the input image pixels provides a faithful measure of pixel importance or saliency for the model's decision."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Gradient ascent on class scores and input-image gradients produce visualizations of ConvNet class notions and saliency maps usable for weakly supervised segmentation."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A convolutional network trained only for image classification can produce saliency maps from class-score gradients that support weakly supervised object segmentation."}],"snapshot_sha256":"8ae9b8c1fbf73e0d3ac5b10982cee3730e0f88187eed2defd8f57ed1b831984d"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"db6fa714dbddd6d366fc2f574dd0f23182e2c37ea7b44e75c1301071b256c6ad"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1312.6034/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper addresses the visualisation of image classification models, learnt using deep Convolutional Networks (ConvNets). We consider two visualisation techniques, based on computing the gradient of the class score with respect to the input image. The first one generates an image, which maximises the class score [Erhan et al., 2009], thus visualising the notion of the class, captured by a ConvNet. The second technique computes a class saliency map, specific to a given image and class. We show that such maps can be employed for weakly supervised object segmentation using classification ConvNe","authors_text":"Andrea Vedaldi, Andrew Zisserman, Karen Simonyan","cross_cats":[],"headline":"A convolutional network trained only for image classification can produce saliency maps from class-score gradients that support weakly supervised object segmentation.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-12-20T16:45:54Z","title":"Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps"},"references":{"count":13,"internal_anchors":0,"resolved_work":13,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, and K.-R. M ¨uller. How to explain individual classiﬁcation decisions. JMLR, 11:1803–1831, 2010","work_id":"f95778af-e182-483e-8c8a-86439e8b2fff","year":2010},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"A. Berg, J. Deng, and L. Fei-Fei. Large scale visual recognition challenge (ILSVRC), 2010. URL http://www.image-net.org/challenges/LSVRC/2010/","work_id":"0339d278-6363-4263-8f54-459ff5ab29ec","year":2010},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Y . Boykov and M. P. Jolly. Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In Proc. ICCV, volume 2, pages 105–112, 2001","work_id":"089346ed-30af-497d-b2a7-ff00b05de5cf","year":2001},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"D. C. Ciresan, U. Meier, and J. Schmidhuber. Multi-column deep neural networks for image classiﬁcation. In Proc. CVPR, pages 3642–3649, 2012","work_id":"01ff0b45-e3b2-4895-bb4e-225f60ede00b","year":2012},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"D. Erhan, Y . Bengio, A. Courville, and P. Vincent. Visualizing higher-layer features of a deep network. Technical Report 1341, University of Montreal, Jun 2009","work_id":"31c921cb-7e81-49b7-b461-b0ebaee63564","year":2009}],"snapshot_sha256":"75c5ca2801a49966e08874edb98644142eebddb1f1e991f62a52229af1be3cf1"},"source":{"id":"1312.6034","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-11T18:45:55.981361Z","id":"672d6331-ac69-47ed-adf5-502aaf3fa7da","model_set":{"reader":"grok-4.3"},"one_line_summary":"Gradient ascent on class scores and input-image gradients produce visualizations of ConvNet class notions and saliency maps usable for weakly supervised segmentation.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A convolutional network trained only for image classification can produce saliency maps from class-score gradients that support weakly supervised object segmentation.","strongest_claim":"We show that such maps can be employed for weakly supervised object segmentation using classification ConvNets.","weakest_assumption":"That the gradient of the class score with respect to the input image pixels provides a faithful measure of pixel importance or saliency for the model's decision."}},"verdict_id":"672d6331-ac69-47ed-adf5-502aaf3fa7da"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:60fcf4fb375725a96d51f4801d3993399c7135633f6d31c83f9c6421afcda955","target":"record","created_at":"2026-07-04T18:53:55Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c6f6267cd2b70914f6018a2479afb24dc3fe836c857e5f9012fd021ce93ab937","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-12-20T16:45:54Z","title_canon_sha256":"6e02324c737aee549d89bbebe4922ce8aa15033116a178446188de2a46d320e7"},"schema_version":"1.0","source":{"id":"1312.6034","kind":"arxiv","version":2}},"canonical_sha256":"0a9a8ba1447120f97e75d0c1aecbdbeb1a7520ac2617791a07191dd35ec8643e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0a9a8ba1447120f97e75d0c1aecbdbeb1a7520ac2617791a07191dd35ec8643e","first_computed_at":"2026-07-04T18:53:55.286564Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T18:53:55.286564Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qkA8YvKxjkCWi/A34j1oUacWksERI8x84Pa7lTNNZFO26ZlsD2AfPpJBTgZb3onfwJ7Sr04ddZmkaxtLty00BA==","signature_status":"signed_v1","signed_at":"2026-07-04T18:53:55.287182Z","signed_message":"canonical_sha256_bytes"},"source_id":"1312.6034","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:60fcf4fb375725a96d51f4801d3993399c7135633f6d31c83f9c6421afcda955","sha256:563d0ad72b91da6b2c12d783fa3d7623d6023bf4cc4a8045bb37de1ea53f46d5"],"state_sha256":"357b9ceb187c774a6ad1c5d6462dca6970b7a74a608b9c085b6c9cadb5baba84"}