{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:N6F4WU6RWNF42ZFGZDHS37UWAY","short_pith_number":"pith:N6F4WU6R","schema_version":"1.0","canonical_sha256":"6f8bcb53d1b34bcd64a6c8cf2dfe96060d2d46df0fb748581b453d08b0c51a85","source":{"kind":"arxiv","id":"2007.07936","version":2},"attestation_state":"computed","paper":{"title":"ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Juliano Pinto, Lennart Svensson, Viktor Olsson, Wilhelm Tranheden","submitted_at":"2020-07-15T18:21:17Z","abstract_excerpt":"The state of the art in semantic segmentation is steadily increasing in performance, resulting in more precise and reliable segmentations in many different applications. However, progress is limited by the cost of generating labels for training, which sometimes requires hours of manual labor for a single image. Because of this, semi-supervised methods have been applied to this task, with varying degrees of success. A key challenge is that common augmentations used in semi-supervised classification are less effective for semantic segmentation. We propose a novel data augmentation mechanism call"},"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":"2007.07936","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-07-15T18:21:17Z","cross_cats_sorted":[],"title_canon_sha256":"07ca78d4ecf46022156e717acf609676e10b8ec05c5a0aaddf31fe64b2297486","abstract_canon_sha256":"6104c533e2451a4acf58561deb740caf17d22e09023d2edabbf92228a2b7fedf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:55:11.729846Z","signature_b64":"FJTOAL97yQSQdbA9RQ+JdmytJAbOYCOrUmwCtwcUGKx2J3JYAXAaPlTjPTWX5wDe7aEhF/xtDPXjnOUkW/zjDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f8bcb53d1b34bcd64a6c8cf2dfe96060d2d46df0fb748581b453d08b0c51a85","last_reissued_at":"2026-07-05T01:55:11.729346Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:55:11.729346Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Juliano Pinto, Lennart Svensson, Viktor Olsson, Wilhelm Tranheden","submitted_at":"2020-07-15T18:21:17Z","abstract_excerpt":"The state of the art in semantic segmentation is steadily increasing in performance, resulting in more precise and reliable segmentations in many different applications. However, progress is limited by the cost of generating labels for training, which sometimes requires hours of manual labor for a single image. Because of this, semi-supervised methods have been applied to this task, with varying degrees of success. A key challenge is that common augmentations used in semi-supervised classification are less effective for semantic segmentation. We propose a novel data augmentation mechanism call"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.07936","kind":"arxiv","version":2},"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/2007.07936/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":"2007.07936","created_at":"2026-07-05T01:55:11.729406+00:00"},{"alias_kind":"arxiv_version","alias_value":"2007.07936v2","created_at":"2026-07-05T01:55:11.729406+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.07936","created_at":"2026-07-05T01:55:11.729406+00:00"},{"alias_kind":"pith_short_12","alias_value":"N6F4WU6RWNF4","created_at":"2026-07-05T01:55:11.729406+00:00"},{"alias_kind":"pith_short_16","alias_value":"N6F4WU6RWNF42ZFG","created_at":"2026-07-05T01:55:11.729406+00:00"},{"alias_kind":"pith_short_8","alias_value":"N6F4WU6R","created_at":"2026-07-05T01:55:11.729406+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/N6F4WU6RWNF42ZFGZDHS37UWAY","json":"https://pith.science/pith/N6F4WU6RWNF42ZFGZDHS37UWAY.json","graph_json":"https://pith.science/api/pith-number/N6F4WU6RWNF42ZFGZDHS37UWAY/graph.json","events_json":"https://pith.science/api/pith-number/N6F4WU6RWNF42ZFGZDHS37UWAY/events.json","paper":"https://pith.science/paper/N6F4WU6R"},"agent_actions":{"view_html":"https://pith.science/pith/N6F4WU6RWNF42ZFGZDHS37UWAY","download_json":"https://pith.science/pith/N6F4WU6RWNF42ZFGZDHS37UWAY.json","view_paper":"https://pith.science/paper/N6F4WU6R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2007.07936&json=true","fetch_graph":"https://pith.science/api/pith-number/N6F4WU6RWNF42ZFGZDHS37UWAY/graph.json","fetch_events":"https://pith.science/api/pith-number/N6F4WU6RWNF42ZFGZDHS37UWAY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N6F4WU6RWNF42ZFGZDHS37UWAY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N6F4WU6RWNF42ZFGZDHS37UWAY/action/storage_attestation","attest_author":"https://pith.science/pith/N6F4WU6RWNF42ZFGZDHS37UWAY/action/author_attestation","sign_citation":"https://pith.science/pith/N6F4WU6RWNF42ZFGZDHS37UWAY/action/citation_signature","submit_replication":"https://pith.science/pith/N6F4WU6RWNF42ZFGZDHS37UWAY/action/replication_record"}},"created_at":"2026-07-05T01:55:11.729406+00:00","updated_at":"2026-07-05T01:55:11.729406+00:00"}