{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:PS4UECYTKMOESGAI3TC3EFXK56","short_pith_number":"pith:PS4UECYT","schema_version":"1.0","canonical_sha256":"7cb9420b13531c491808dcc5b216eaefb180dbcbaf2589360e7c0f5290569b1c","source":{"kind":"arxiv","id":"2011.08341","version":1},"attestation_state":"computed","paper":{"title":"Robust Deep Learning with Active Noise Cancellation for Spatial Computing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"David Yang, Ilknur Kabul, Li Chen, Purvi Goel","submitted_at":"2020-11-16T23:56:14Z","abstract_excerpt":"This paper proposes CANC, a Co-teaching Active Noise Cancellation method, applied in spatial computing to address deep learning trained with extreme noisy labels. Deep learning algorithms have been successful in spatial computing for land or building footprint recognition. However a lot of noise exists in ground truth labels due to how labels are collected in spatial computing and satellite imagery. Existing methods to deal with extreme label noise conduct clean sample selection and do not utilize the remaining samples. Such techniques can be wasteful due to the cost of data retrieval. Our pro"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2011.08341","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-16T23:56:14Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"f29e39084a02ceb67adec0e0f919513c4946f305c8a745a77139f120ddc394d5","abstract_canon_sha256":"08427be529852f00b1f016d6b88fc446fad5c517a0dcc201f6accffe26350758"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:52:04.330873Z","signature_b64":"VpyI/8nDCxQzh1A2ibYGIvrqeqfNITciTm2XWpZbu9zJlBnPnw/2Kpvo8uYiLyI2e8ZQIOVRfI5RRTHxUZEoBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7cb9420b13531c491808dcc5b216eaefb180dbcbaf2589360e7c0f5290569b1c","last_reissued_at":"2026-07-05T01:52:04.330431Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:52:04.330431Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Deep Learning with Active Noise Cancellation for Spatial Computing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"David Yang, Ilknur Kabul, Li Chen, Purvi Goel","submitted_at":"2020-11-16T23:56:14Z","abstract_excerpt":"This paper proposes CANC, a Co-teaching Active Noise Cancellation method, applied in spatial computing to address deep learning trained with extreme noisy labels. Deep learning algorithms have been successful in spatial computing for land or building footprint recognition. However a lot of noise exists in ground truth labels due to how labels are collected in spatial computing and satellite imagery. Existing methods to deal with extreme label noise conduct clean sample selection and do not utilize the remaining samples. Such techniques can be wasteful due to the cost of data retrieval. Our pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.08341","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/2011.08341/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2011.08341","created_at":"2026-07-05T01:52:04.330489+00:00"},{"alias_kind":"arxiv_version","alias_value":"2011.08341v1","created_at":"2026-07-05T01:52:04.330489+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.08341","created_at":"2026-07-05T01:52:04.330489+00:00"},{"alias_kind":"pith_short_12","alias_value":"PS4UECYTKMOE","created_at":"2026-07-05T01:52:04.330489+00:00"},{"alias_kind":"pith_short_16","alias_value":"PS4UECYTKMOESGAI","created_at":"2026-07-05T01:52:04.330489+00:00"},{"alias_kind":"pith_short_8","alias_value":"PS4UECYT","created_at":"2026-07-05T01:52:04.330489+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PS4UECYTKMOESGAI3TC3EFXK56","json":"https://pith.science/pith/PS4UECYTKMOESGAI3TC3EFXK56.json","graph_json":"https://pith.science/api/pith-number/PS4UECYTKMOESGAI3TC3EFXK56/graph.json","events_json":"https://pith.science/api/pith-number/PS4UECYTKMOESGAI3TC3EFXK56/events.json","paper":"https://pith.science/paper/PS4UECYT"},"agent_actions":{"view_html":"https://pith.science/pith/PS4UECYTKMOESGAI3TC3EFXK56","download_json":"https://pith.science/pith/PS4UECYTKMOESGAI3TC3EFXK56.json","view_paper":"https://pith.science/paper/PS4UECYT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2011.08341&json=true","fetch_graph":"https://pith.science/api/pith-number/PS4UECYTKMOESGAI3TC3EFXK56/graph.json","fetch_events":"https://pith.science/api/pith-number/PS4UECYTKMOESGAI3TC3EFXK56/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PS4UECYTKMOESGAI3TC3EFXK56/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PS4UECYTKMOESGAI3TC3EFXK56/action/storage_attestation","attest_author":"https://pith.science/pith/PS4UECYTKMOESGAI3TC3EFXK56/action/author_attestation","sign_citation":"https://pith.science/pith/PS4UECYTKMOESGAI3TC3EFXK56/action/citation_signature","submit_replication":"https://pith.science/pith/PS4UECYTKMOESGAI3TC3EFXK56/action/replication_record"}},"created_at":"2026-07-05T01:52:04.330489+00:00","updated_at":"2026-07-05T01:52:04.330489+00:00"}