{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:ISRTTJHVEWYN5DCIEA6U4VS5Z2","short_pith_number":"pith:ISRTTJHV","schema_version":"1.0","canonical_sha256":"44a339a4f525b0de8c48203d4e565dce83a0a1fbfd8373d9149e36f45cd789a2","source":{"kind":"arxiv","id":"1301.2840","version":4},"attestation_state":"computed","paper":{"title":"Unsupervised Feature Learning for low-level Local Image Descriptors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Christian Osendorfer, Justin Bayer, Patrick van der Smagt, Sebastian Urban","submitted_at":"2013-01-14T01:34:17Z","abstract_excerpt":"Unsupervised feature learning has shown impressive results for a wide range of input modalities, in particular for object classification tasks in computer vision. Using a large amount of unlabeled data, unsupervised feature learning methods are utilized to construct high-level representations that are discriminative enough for subsequently trained supervised classification algorithms. However, it has never been \\emph{quantitatively} investigated yet how well unsupervised learning methods can find \\emph{low-level representations} for image patches without any additional supervision. In this pap"},"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":"1301.2840","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-01-14T01:34:17Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"07a89ec6903a8e04784cf7236162e3be96a931111555d21d38aa6405196c3c63","abstract_canon_sha256":"e122acab776ba7e796e88775cd9131d5915f7e343c8c41ead05216624320b709"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:27:11.246307Z","signature_b64":"StzU3ajSGU2bg993ApClkzZEZuQ+fSDV/yLR8w6RGpGKOpQMNVSsszi/2M6eR0QKwJF4NZf+VhCwAIjplTsWDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"44a339a4f525b0de8c48203d4e565dce83a0a1fbfd8373d9149e36f45cd789a2","last_reissued_at":"2026-05-18T03:27:11.245644Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:27:11.245644Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised Feature Learning for low-level Local Image Descriptors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Christian Osendorfer, Justin Bayer, Patrick van der Smagt, Sebastian Urban","submitted_at":"2013-01-14T01:34:17Z","abstract_excerpt":"Unsupervised feature learning has shown impressive results for a wide range of input modalities, in particular for object classification tasks in computer vision. Using a large amount of unlabeled data, unsupervised feature learning methods are utilized to construct high-level representations that are discriminative enough for subsequently trained supervised classification algorithms. However, it has never been \\emph{quantitatively} investigated yet how well unsupervised learning methods can find \\emph{low-level representations} for image patches without any additional supervision. In this pap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1301.2840","kind":"arxiv","version":4},"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":"1301.2840","created_at":"2026-05-18T03:27:11.245771+00:00"},{"alias_kind":"arxiv_version","alias_value":"1301.2840v4","created_at":"2026-05-18T03:27:11.245771+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1301.2840","created_at":"2026-05-18T03:27:11.245771+00:00"},{"alias_kind":"pith_short_12","alias_value":"ISRTTJHVEWYN","created_at":"2026-05-18T12:27:49.015174+00:00"},{"alias_kind":"pith_short_16","alias_value":"ISRTTJHVEWYN5DCI","created_at":"2026-05-18T12:27:49.015174+00:00"},{"alias_kind":"pith_short_8","alias_value":"ISRTTJHV","created_at":"2026-05-18T12:27:49.015174+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/ISRTTJHVEWYN5DCIEA6U4VS5Z2","json":"https://pith.science/pith/ISRTTJHVEWYN5DCIEA6U4VS5Z2.json","graph_json":"https://pith.science/api/pith-number/ISRTTJHVEWYN5DCIEA6U4VS5Z2/graph.json","events_json":"https://pith.science/api/pith-number/ISRTTJHVEWYN5DCIEA6U4VS5Z2/events.json","paper":"https://pith.science/paper/ISRTTJHV"},"agent_actions":{"view_html":"https://pith.science/pith/ISRTTJHVEWYN5DCIEA6U4VS5Z2","download_json":"https://pith.science/pith/ISRTTJHVEWYN5DCIEA6U4VS5Z2.json","view_paper":"https://pith.science/paper/ISRTTJHV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1301.2840&json=true","fetch_graph":"https://pith.science/api/pith-number/ISRTTJHVEWYN5DCIEA6U4VS5Z2/graph.json","fetch_events":"https://pith.science/api/pith-number/ISRTTJHVEWYN5DCIEA6U4VS5Z2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ISRTTJHVEWYN5DCIEA6U4VS5Z2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ISRTTJHVEWYN5DCIEA6U4VS5Z2/action/storage_attestation","attest_author":"https://pith.science/pith/ISRTTJHVEWYN5DCIEA6U4VS5Z2/action/author_attestation","sign_citation":"https://pith.science/pith/ISRTTJHVEWYN5DCIEA6U4VS5Z2/action/citation_signature","submit_replication":"https://pith.science/pith/ISRTTJHVEWYN5DCIEA6U4VS5Z2/action/replication_record"}},"created_at":"2026-05-18T03:27:11.245771+00:00","updated_at":"2026-05-18T03:27:11.245771+00:00"}