{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:YZRKBTDMU5JMBUW5IKFJKHIUT3","short_pith_number":"pith:YZRKBTDM","canonical_record":{"source":{"id":"1802.03495","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-10T01:33:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e3c3b601ff1b5fc4f0d77555b099c585d408e777c6dcedb12f914e2605398c84","abstract_canon_sha256":"64e8818a610757d7c2520f9b958fe5cf96c014564e46bef3b74250425719a565"},"schema_version":"1.0"},"canonical_sha256":"c662a0cc6ca752c0d2dd428a951d149ece7ffb56f358af6129656ec7388dd593","source":{"kind":"arxiv","id":"1802.03495","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.03495","created_at":"2026-05-18T00:23:51Z"},{"alias_kind":"arxiv_version","alias_value":"1802.03495v1","created_at":"2026-05-18T00:23:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.03495","created_at":"2026-05-18T00:23:51Z"},{"alias_kind":"pith_short_12","alias_value":"YZRKBTDMU5JM","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"YZRKBTDMU5JMBUW5","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"YZRKBTDM","created_at":"2026-05-18T12:33:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:YZRKBTDMU5JMBUW5IKFJKHIUT3","target":"record","payload":{"canonical_record":{"source":{"id":"1802.03495","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-10T01:33:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e3c3b601ff1b5fc4f0d77555b099c585d408e777c6dcedb12f914e2605398c84","abstract_canon_sha256":"64e8818a610757d7c2520f9b958fe5cf96c014564e46bef3b74250425719a565"},"schema_version":"1.0"},"canonical_sha256":"c662a0cc6ca752c0d2dd428a951d149ece7ffb56f358af6129656ec7388dd593","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:51.415517Z","signature_b64":"gwwiX6H1BHT0pfJFCmNfJVoke5n6qAncQaEVh5p5j11grmxXBM0QGaBd5ZI0MNOdakAw1WymrzeU0gN55Rp8DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c662a0cc6ca752c0d2dd428a951d149ece7ffb56f358af6129656ec7388dd593","last_reissued_at":"2026-05-18T00:23:51.414926Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:51.414926Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.03495","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":"2026-05-18T00:23:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xfExDDTRz5mnUGAVkpwC+Fx56YfVjIdVDI9ZefbrXO4rwumuU95bjA695MH4GIiEzd5PR1ko1IFG8zoq1//iBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T13:18:07.424752Z"},"content_sha256":"bdf0671a86c886492c8e8c7876f2233315f253d5eb7680c05c5df1f944106b27","schema_version":"1.0","event_id":"sha256:bdf0671a86c886492c8e8c7876f2233315f253d5eb7680c05c5df1f944106b27"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:YZRKBTDMU5JMBUW5IKFJKHIUT3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generative Adversarial Networks and Probabilistic Graph Models for Hyperspectral Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Jonathan Li, Zilong Zhong","submitted_at":"2018-02-10T01:33:52Z","abstract_excerpt":"High spectral dimensionality and the shortage of annotations make hyperspectral image (HSI) classification a challenging problem. Recent studies suggest that convolutional neural networks can learn discriminative spatial features, which play a paramount role in HSI interpretation. However, most of these methods ignore the distinctive spectral-spatial characteristic of hyperspectral data. In addition, a large amount of unlabeled data remains an unexploited gold mine for efficient data use. Therefore, we proposed an integration of generative adversarial networks (GANs) and probabilistic graphica"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.03495","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":"2026-05-18T00:23:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qAKdz0OmtG5mLlb5XTn6tRE2pXxC3/wSoUgUrE8e72RIeN6Io6CoUXLW8ya9cfxbBaCJJqI3RHyEpB7egMRxDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T13:18:07.425098Z"},"content_sha256":"a9ab9c59b8122cbfbe0b52772d5b834510db8cdf1455e387923d58811a4cacf1","schema_version":"1.0","event_id":"sha256:a9ab9c59b8122cbfbe0b52772d5b834510db8cdf1455e387923d58811a4cacf1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YZRKBTDMU5JMBUW5IKFJKHIUT3/bundle.json","state_url":"https://pith.science/pith/YZRKBTDMU5JMBUW5IKFJKHIUT3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YZRKBTDMU5JMBUW5IKFJKHIUT3/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":"2026-07-04T13:18:07Z","links":{"resolver":"https://pith.science/pith/YZRKBTDMU5JMBUW5IKFJKHIUT3","bundle":"https://pith.science/pith/YZRKBTDMU5JMBUW5IKFJKHIUT3/bundle.json","state":"https://pith.science/pith/YZRKBTDMU5JMBUW5IKFJKHIUT3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YZRKBTDMU5JMBUW5IKFJKHIUT3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:YZRKBTDMU5JMBUW5IKFJKHIUT3","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":"64e8818a610757d7c2520f9b958fe5cf96c014564e46bef3b74250425719a565","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-10T01:33:52Z","title_canon_sha256":"e3c3b601ff1b5fc4f0d77555b099c585d408e777c6dcedb12f914e2605398c84"},"schema_version":"1.0","source":{"id":"1802.03495","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.03495","created_at":"2026-05-18T00:23:51Z"},{"alias_kind":"arxiv_version","alias_value":"1802.03495v1","created_at":"2026-05-18T00:23:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.03495","created_at":"2026-05-18T00:23:51Z"},{"alias_kind":"pith_short_12","alias_value":"YZRKBTDMU5JM","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"YZRKBTDMU5JMBUW5","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"YZRKBTDM","created_at":"2026-05-18T12:33:04Z"}],"graph_snapshots":[{"event_id":"sha256:a9ab9c59b8122cbfbe0b52772d5b834510db8cdf1455e387923d58811a4cacf1","target":"graph","created_at":"2026-05-18T00:23:51Z","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":"High spectral dimensionality and the shortage of annotations make hyperspectral image (HSI) classification a challenging problem. Recent studies suggest that convolutional neural networks can learn discriminative spatial features, which play a paramount role in HSI interpretation. However, most of these methods ignore the distinctive spectral-spatial characteristic of hyperspectral data. In addition, a large amount of unlabeled data remains an unexploited gold mine for efficient data use. Therefore, we proposed an integration of generative adversarial networks (GANs) and probabilistic graphica","authors_text":"Jonathan Li, Zilong Zhong","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-10T01:33:52Z","title":"Generative Adversarial Networks and Probabilistic Graph Models for Hyperspectral Image Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.03495","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:bdf0671a86c886492c8e8c7876f2233315f253d5eb7680c05c5df1f944106b27","target":"record","created_at":"2026-05-18T00:23:51Z","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":"64e8818a610757d7c2520f9b958fe5cf96c014564e46bef3b74250425719a565","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-10T01:33:52Z","title_canon_sha256":"e3c3b601ff1b5fc4f0d77555b099c585d408e777c6dcedb12f914e2605398c84"},"schema_version":"1.0","source":{"id":"1802.03495","kind":"arxiv","version":1}},"canonical_sha256":"c662a0cc6ca752c0d2dd428a951d149ece7ffb56f358af6129656ec7388dd593","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c662a0cc6ca752c0d2dd428a951d149ece7ffb56f358af6129656ec7388dd593","first_computed_at":"2026-05-18T00:23:51.414926Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:23:51.414926Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gwwiX6H1BHT0pfJFCmNfJVoke5n6qAncQaEVh5p5j11grmxXBM0QGaBd5ZI0MNOdakAw1WymrzeU0gN55Rp8DA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:23:51.415517Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.03495","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bdf0671a86c886492c8e8c7876f2233315f253d5eb7680c05c5df1f944106b27","sha256:a9ab9c59b8122cbfbe0b52772d5b834510db8cdf1455e387923d58811a4cacf1"],"state_sha256":"bb40d9c18d7b482ee7f1ce0837d5aa959ebe2dfa3c9a3a170455b4271fa0faa3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QjA8kaYlMPc4DODBk2/XWIrk8UTEVU+jDGp/DA7DLLxVpFP2iPx7S7UeESOQn3N0ETzgPeCPmlbZj7jkbYO4CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-04T13:18:07.426996Z","bundle_sha256":"175976e86a0af9b750deffee08b543de13ae0a9f5d4c964c29154c7d45f532a1"}}