{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:LNDPY5KQNDVM6AFGZKXVEBZ2SQ","short_pith_number":"pith:LNDPY5KQ","canonical_record":{"source":{"id":"1707.09119","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-28T06:41:34Z","cross_cats_sorted":[],"title_canon_sha256":"f1ab0755b9fd84130074cf02790986683ae5a1eedb35931359804a6b61946e84","abstract_canon_sha256":"b674fca96b0270eabb838185945739409da79e10d26f7f5f793efe074a9d24c7"},"schema_version":"1.0"},"canonical_sha256":"5b46fc755068eacf00a6caaf52073a942bcb9012f0e0611eb67e7b75d95e21f0","source":{"kind":"arxiv","id":"1707.09119","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.09119","created_at":"2026-05-18T00:39:11Z"},{"alias_kind":"arxiv_version","alias_value":"1707.09119v1","created_at":"2026-05-18T00:39:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.09119","created_at":"2026-05-18T00:39:11Z"},{"alias_kind":"pith_short_12","alias_value":"LNDPY5KQNDVM","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"LNDPY5KQNDVM6AFG","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"LNDPY5KQ","created_at":"2026-05-18T12:31:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:LNDPY5KQNDVM6AFGZKXVEBZ2SQ","target":"record","payload":{"canonical_record":{"source":{"id":"1707.09119","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-28T06:41:34Z","cross_cats_sorted":[],"title_canon_sha256":"f1ab0755b9fd84130074cf02790986683ae5a1eedb35931359804a6b61946e84","abstract_canon_sha256":"b674fca96b0270eabb838185945739409da79e10d26f7f5f793efe074a9d24c7"},"schema_version":"1.0"},"canonical_sha256":"5b46fc755068eacf00a6caaf52073a942bcb9012f0e0611eb67e7b75d95e21f0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:11.653853Z","signature_b64":"MEXegqJwEcrKaiiDxpsAn4YWfbo66ugO5c47kugiJ7SEEWJ57UCuDxEhnL72AuKfKt5TZBzbeJ1n9wCNVY3gCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b46fc755068eacf00a6caaf52073a942bcb9012f0e0611eb67e7b75d95e21f0","last_reissued_at":"2026-05-18T00:39:11.653112Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:11.653112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1707.09119","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:39:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NLSDl437sVcK02nNWGwAvSVyju7coB4+df+xOhundSbeRvjaPwLNEcPMDxMiqAV0/0XcP2N+zYNgmtWpQPAXBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T08:54:36.505394Z"},"content_sha256":"a6faf021a7c510c4c0aa5c1fc08f6ff8057446c092248d8ce3e6440fec168853","schema_version":"1.0","event_id":"sha256:a6faf021a7c510c4c0aa5c1fc08f6ff8057446c092248d8ce3e6440fec168853"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:LNDPY5KQNDVM6AFGZKXVEBZ2SQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Co-Space: Sample Mining Across Feature Transformation for Semi-Supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ebroul Izquierdo, Keze Wang, Liang Lin, Pai Peng, Xiao Wang, Ziliang Chen","submitted_at":"2017-07-28T06:41:34Z","abstract_excerpt":"Aiming at improving performance of visual classification in a cost-effective manner, this paper proposes an incremental semi-supervised learning paradigm called Deep Co-Space (DCS). Unlike many conventional semi-supervised learning methods usually performing within a fixed feature space, our DCS gradually propagates information from labeled samples to unlabeled ones along with deep feature learning. We regard deep feature learning as a series of steps pursuing feature transformation, i.e., projecting the samples from a previous space into a new one, which tends to select the reliable unlabeled"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.09119","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:39:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MY6OCjtV8nkpoRWUdpc/q8WLDZKTWo5j/usPNT6wnL0m4dm1+KfBXfdoE4wKPtOsREDH3YTLuHknv3sZcv0xDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T08:54:36.506119Z"},"content_sha256":"4dd06c7305da10841dc263c5806364e1e523abc933dc4ee558186757b5ace6e1","schema_version":"1.0","event_id":"sha256:4dd06c7305da10841dc263c5806364e1e523abc933dc4ee558186757b5ace6e1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LNDPY5KQNDVM6AFGZKXVEBZ2SQ/bundle.json","state_url":"https://pith.science/pith/LNDPY5KQNDVM6AFGZKXVEBZ2SQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LNDPY5KQNDVM6AFGZKXVEBZ2SQ/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":"2026-06-09T08:54:36Z","links":{"resolver":"https://pith.science/pith/LNDPY5KQNDVM6AFGZKXVEBZ2SQ","bundle":"https://pith.science/pith/LNDPY5KQNDVM6AFGZKXVEBZ2SQ/bundle.json","state":"https://pith.science/pith/LNDPY5KQNDVM6AFGZKXVEBZ2SQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LNDPY5KQNDVM6AFGZKXVEBZ2SQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:LNDPY5KQNDVM6AFGZKXVEBZ2SQ","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":"b674fca96b0270eabb838185945739409da79e10d26f7f5f793efe074a9d24c7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-28T06:41:34Z","title_canon_sha256":"f1ab0755b9fd84130074cf02790986683ae5a1eedb35931359804a6b61946e84"},"schema_version":"1.0","source":{"id":"1707.09119","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.09119","created_at":"2026-05-18T00:39:11Z"},{"alias_kind":"arxiv_version","alias_value":"1707.09119v1","created_at":"2026-05-18T00:39:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.09119","created_at":"2026-05-18T00:39:11Z"},{"alias_kind":"pith_short_12","alias_value":"LNDPY5KQNDVM","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"LNDPY5KQNDVM6AFG","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"LNDPY5KQ","created_at":"2026-05-18T12:31:28Z"}],"graph_snapshots":[{"event_id":"sha256:4dd06c7305da10841dc263c5806364e1e523abc933dc4ee558186757b5ace6e1","target":"graph","created_at":"2026-05-18T00:39:11Z","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":"Aiming at improving performance of visual classification in a cost-effective manner, this paper proposes an incremental semi-supervised learning paradigm called Deep Co-Space (DCS). Unlike many conventional semi-supervised learning methods usually performing within a fixed feature space, our DCS gradually propagates information from labeled samples to unlabeled ones along with deep feature learning. We regard deep feature learning as a series of steps pursuing feature transformation, i.e., projecting the samples from a previous space into a new one, which tends to select the reliable unlabeled","authors_text":"Ebroul Izquierdo, Keze Wang, Liang Lin, Pai Peng, Xiao Wang, Ziliang Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-28T06:41:34Z","title":"Deep Co-Space: Sample Mining Across Feature Transformation for Semi-Supervised Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.09119","kind":"arxiv","version":1},"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:a6faf021a7c510c4c0aa5c1fc08f6ff8057446c092248d8ce3e6440fec168853","target":"record","created_at":"2026-05-18T00:39:11Z","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":"b674fca96b0270eabb838185945739409da79e10d26f7f5f793efe074a9d24c7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-28T06:41:34Z","title_canon_sha256":"f1ab0755b9fd84130074cf02790986683ae5a1eedb35931359804a6b61946e84"},"schema_version":"1.0","source":{"id":"1707.09119","kind":"arxiv","version":1}},"canonical_sha256":"5b46fc755068eacf00a6caaf52073a942bcb9012f0e0611eb67e7b75d95e21f0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5b46fc755068eacf00a6caaf52073a942bcb9012f0e0611eb67e7b75d95e21f0","first_computed_at":"2026-05-18T00:39:11.653112Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:39:11.653112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MEXegqJwEcrKaiiDxpsAn4YWfbo66ugO5c47kugiJ7SEEWJ57UCuDxEhnL72AuKfKt5TZBzbeJ1n9wCNVY3gCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:39:11.653853Z","signed_message":"canonical_sha256_bytes"},"source_id":"1707.09119","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a6faf021a7c510c4c0aa5c1fc08f6ff8057446c092248d8ce3e6440fec168853","sha256:4dd06c7305da10841dc263c5806364e1e523abc933dc4ee558186757b5ace6e1"],"state_sha256":"ef69634cc9c4e31f7c3ecc4768076ec38883b59dd5c45fe943f87f4d21d176d2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mwe6Cqg+KzSekTaA+M3jdTs/J9xYRY79Tc87jsF0btJoV0SRgrovLAoVSml31mIGJuvmQn1NHApXCH3eubBODA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T08:54:36.510611Z","bundle_sha256":"dd4f2ea4db64555d4a29bf8f79bc0e683bc82233a1dc5c1a4e31b55f74c618c6"}}