{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:Y3ZBQE5XA525YSNOWYXBMAEEWM","short_pith_number":"pith:Y3ZBQE5X","canonical_record":{"source":{"id":"1703.06000","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-17T13:14:36Z","cross_cats_sorted":[],"title_canon_sha256":"0305757abfd87b1d2b207c94754148ae66bd2f0c0baf32269ff8618b43ead188","abstract_canon_sha256":"8ecfb9c90eaa361119d53ab5a5a2b542f8a5367b66cfa12cd158e4ccaf1c1cde"},"schema_version":"1.0"},"canonical_sha256":"c6f21813b70775dc49aeb62e160084b33f1a0b59ba474b6295bc453786c23a2e","source":{"kind":"arxiv","id":"1703.06000","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.06000","created_at":"2026-05-17T23:48:17Z"},{"alias_kind":"arxiv_version","alias_value":"1703.06000v2","created_at":"2026-05-17T23:48:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.06000","created_at":"2026-05-17T23:48:17Z"},{"alias_kind":"pith_short_12","alias_value":"Y3ZBQE5XA525","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"Y3ZBQE5XA525YSNO","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"Y3ZBQE5X","created_at":"2026-05-18T12:31:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:Y3ZBQE5XA525YSNOWYXBMAEEWM","target":"record","payload":{"canonical_record":{"source":{"id":"1703.06000","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-17T13:14:36Z","cross_cats_sorted":[],"title_canon_sha256":"0305757abfd87b1d2b207c94754148ae66bd2f0c0baf32269ff8618b43ead188","abstract_canon_sha256":"8ecfb9c90eaa361119d53ab5a5a2b542f8a5367b66cfa12cd158e4ccaf1c1cde"},"schema_version":"1.0"},"canonical_sha256":"c6f21813b70775dc49aeb62e160084b33f1a0b59ba474b6295bc453786c23a2e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:17.667352Z","signature_b64":"ywIubPRnimPHzyhbk/ilUDIMzgA8FLCgoYGa+NT9rpR/UAcxSZqLbgz/Yx468uXBW/I6P47Bcq9DvRFSFpZbAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6f21813b70775dc49aeb62e160084b33f1a0b59ba474b6295bc453786c23a2e","last_reissued_at":"2026-05-17T23:48:17.666666Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:17.666666Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.06000","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:48:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kx9MjuL/eN6Je12Ri/YQdTuYcKqiTqMfnB3lvYofCiVZvNg//XOJHJs4ei7RgWy5HWwclXnahte8PV3DtiEKAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T14:44:14.539881Z"},"content_sha256":"5d2b8e10f28982c1f1ded61c6058377d3fa62a1110e78d34abb8ea7137abe1ac","schema_version":"1.0","event_id":"sha256:5d2b8e10f28982c1f1ded61c6058377d3fa62a1110e78d34abb8ea7137abe1ac"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:Y3ZBQE5XA525YSNOWYXBMAEEWM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semi-Supervised Deep Learning for Fully Convolutional Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Christoph Baur, Nassir Navab, Shadi Albarqouni","submitted_at":"2017-03-17T13:14:36Z","abstract_excerpt":"Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training. Recently, semi-supervised deep learning has been intensively studied for standard CNN architectures. However, Fully Convolutional Networks (FCNs) set the state-of-the-art for many image segmentation tasks. To the best of our knowledge, there is no existing semi-supervised learning method for such FCNs yet. We lift the concept of auxiliary manif"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.06000","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:48:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SsKaZV7cXES5U/C//4rKga9di3arfGr+RujhN/wIs72lwRRpkfaqCo+HIqssdz6u4coGRvusxhRq6ryXQJQcAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T14:44:14.540244Z"},"content_sha256":"3c098dcd80f550553a8d01d8bd881ae46aff31d9bafd452dd133bc4327b7c2ee","schema_version":"1.0","event_id":"sha256:3c098dcd80f550553a8d01d8bd881ae46aff31d9bafd452dd133bc4327b7c2ee"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y3ZBQE5XA525YSNOWYXBMAEEWM/bundle.json","state_url":"https://pith.science/pith/Y3ZBQE5XA525YSNOWYXBMAEEWM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y3ZBQE5XA525YSNOWYXBMAEEWM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-08T14:44:14Z","links":{"resolver":"https://pith.science/pith/Y3ZBQE5XA525YSNOWYXBMAEEWM","bundle":"https://pith.science/pith/Y3ZBQE5XA525YSNOWYXBMAEEWM/bundle.json","state":"https://pith.science/pith/Y3ZBQE5XA525YSNOWYXBMAEEWM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y3ZBQE5XA525YSNOWYXBMAEEWM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:Y3ZBQE5XA525YSNOWYXBMAEEWM","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":"8ecfb9c90eaa361119d53ab5a5a2b542f8a5367b66cfa12cd158e4ccaf1c1cde","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-17T13:14:36Z","title_canon_sha256":"0305757abfd87b1d2b207c94754148ae66bd2f0c0baf32269ff8618b43ead188"},"schema_version":"1.0","source":{"id":"1703.06000","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.06000","created_at":"2026-05-17T23:48:17Z"},{"alias_kind":"arxiv_version","alias_value":"1703.06000v2","created_at":"2026-05-17T23:48:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.06000","created_at":"2026-05-17T23:48:17Z"},{"alias_kind":"pith_short_12","alias_value":"Y3ZBQE5XA525","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"Y3ZBQE5XA525YSNO","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"Y3ZBQE5X","created_at":"2026-05-18T12:31:56Z"}],"graph_snapshots":[{"event_id":"sha256:3c098dcd80f550553a8d01d8bd881ae46aff31d9bafd452dd133bc4327b7c2ee","target":"graph","created_at":"2026-05-17T23:48:17Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training. Recently, semi-supervised deep learning has been intensively studied for standard CNN architectures. However, Fully Convolutional Networks (FCNs) set the state-of-the-art for many image segmentation tasks. To the best of our knowledge, there is no existing semi-supervised learning method for such FCNs yet. We lift the concept of auxiliary manif","authors_text":"Christoph Baur, Nassir Navab, Shadi Albarqouni","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-17T13:14:36Z","title":"Semi-Supervised Deep Learning for Fully Convolutional Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.06000","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5d2b8e10f28982c1f1ded61c6058377d3fa62a1110e78d34abb8ea7137abe1ac","target":"record","created_at":"2026-05-17T23:48:17Z","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":"8ecfb9c90eaa361119d53ab5a5a2b542f8a5367b66cfa12cd158e4ccaf1c1cde","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-17T13:14:36Z","title_canon_sha256":"0305757abfd87b1d2b207c94754148ae66bd2f0c0baf32269ff8618b43ead188"},"schema_version":"1.0","source":{"id":"1703.06000","kind":"arxiv","version":2}},"canonical_sha256":"c6f21813b70775dc49aeb62e160084b33f1a0b59ba474b6295bc453786c23a2e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c6f21813b70775dc49aeb62e160084b33f1a0b59ba474b6295bc453786c23a2e","first_computed_at":"2026-05-17T23:48:17.666666Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:48:17.666666Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ywIubPRnimPHzyhbk/ilUDIMzgA8FLCgoYGa+NT9rpR/UAcxSZqLbgz/Yx468uXBW/I6P47Bcq9DvRFSFpZbAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:48:17.667352Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.06000","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5d2b8e10f28982c1f1ded61c6058377d3fa62a1110e78d34abb8ea7137abe1ac","sha256:3c098dcd80f550553a8d01d8bd881ae46aff31d9bafd452dd133bc4327b7c2ee"],"state_sha256":"fab29ec37c8c0f45e1f60101470a39d242d4f9acbace33aa6ea32e3a74585716"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TP/CXFXiV8QogwkP6ZVt9xtnOrqC9aiBRTzXRCGf5iMjCq8cIo1vRUra7OnTmaWnp44JNcjvYmyN1uhOtLhWCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T14:44:14.542345Z","bundle_sha256":"09d43497c3b7ceeb4bdd3dd0c923cdf1ea502034ccace3f0783d8552784ce321"}}