{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:C2PXB5S5AVACVCLEM24KXJRIBF","short_pith_number":"pith:C2PXB5S5","schema_version":"1.0","canonical_sha256":"169f70f65d05402a896466b8aba6280961239c9842948872afe486b7fba5b7e6","source":{"kind":"arxiv","id":"1612.09134","version":1},"attestation_state":"computed","paper":{"title":"From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Antonio M. Lopez, David Vazquez, German Ros, Jiaolong Xu, Jose L. Gomez","submitted_at":"2016-12-29T13:16:22Z","abstract_excerpt":"Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing visual perception challenges, such as localizing certain object classes within an image, the learning of the involved classifiers turns out to be a practical bottleneck. The reason is that, at least, we have to frame object examples with bounding boxes in thousands of images. A priori, the more complex the model is regarding its number of parameters, the more annotated examples are required. This annotation task is "},"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":"1612.09134","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-29T13:16:22Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"419c265f4f5cb03e19ab904160ce29142cf8e88c698056036b12393ccaa63b20","abstract_canon_sha256":"c22af8f6c4edf1442d01ca4aa775dd40e7e74fc568030da955c91ce6c54d9549"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:53:46.390228Z","signature_b64":"NCzsoWf8/j5glqXYt9RKM16331yU//9o9nW2Vk+iulP74QSx70EOvlwEEQFGq0bR2nFtaO+X07bHjRI2DSmxDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"169f70f65d05402a896466b8aba6280961239c9842948872afe486b7fba5b7e6","last_reissued_at":"2026-05-18T00:53:46.389844Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:53:46.389844Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Antonio M. Lopez, David Vazquez, German Ros, Jiaolong Xu, Jose L. Gomez","submitted_at":"2016-12-29T13:16:22Z","abstract_excerpt":"Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing visual perception challenges, such as localizing certain object classes within an image, the learning of the involved classifiers turns out to be a practical bottleneck. The reason is that, at least, we have to frame object examples with bounding boxes in thousands of images. A priori, the more complex the model is regarding its number of parameters, the more annotated examples are required. This annotation task is "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.09134","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":""},"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":"1612.09134","created_at":"2026-05-18T00:53:46.389907+00:00"},{"alias_kind":"arxiv_version","alias_value":"1612.09134v1","created_at":"2026-05-18T00:53:46.389907+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.09134","created_at":"2026-05-18T00:53:46.389907+00:00"},{"alias_kind":"pith_short_12","alias_value":"C2PXB5S5AVAC","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_16","alias_value":"C2PXB5S5AVACVCLE","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_8","alias_value":"C2PXB5S5","created_at":"2026-05-18T12:30:09.641336+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/C2PXB5S5AVACVCLEM24KXJRIBF","json":"https://pith.science/pith/C2PXB5S5AVACVCLEM24KXJRIBF.json","graph_json":"https://pith.science/api/pith-number/C2PXB5S5AVACVCLEM24KXJRIBF/graph.json","events_json":"https://pith.science/api/pith-number/C2PXB5S5AVACVCLEM24KXJRIBF/events.json","paper":"https://pith.science/paper/C2PXB5S5"},"agent_actions":{"view_html":"https://pith.science/pith/C2PXB5S5AVACVCLEM24KXJRIBF","download_json":"https://pith.science/pith/C2PXB5S5AVACVCLEM24KXJRIBF.json","view_paper":"https://pith.science/paper/C2PXB5S5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1612.09134&json=true","fetch_graph":"https://pith.science/api/pith-number/C2PXB5S5AVACVCLEM24KXJRIBF/graph.json","fetch_events":"https://pith.science/api/pith-number/C2PXB5S5AVACVCLEM24KXJRIBF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C2PXB5S5AVACVCLEM24KXJRIBF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C2PXB5S5AVACVCLEM24KXJRIBF/action/storage_attestation","attest_author":"https://pith.science/pith/C2PXB5S5AVACVCLEM24KXJRIBF/action/author_attestation","sign_citation":"https://pith.science/pith/C2PXB5S5AVACVCLEM24KXJRIBF/action/citation_signature","submit_replication":"https://pith.science/pith/C2PXB5S5AVACVCLEM24KXJRIBF/action/replication_record"}},"created_at":"2026-05-18T00:53:46.389907+00:00","updated_at":"2026-05-18T00:53:46.389907+00:00"}