{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:V4SV52ADHML2ZZFXOVOUBQMWAC","short_pith_number":"pith:V4SV52AD","schema_version":"1.0","canonical_sha256":"af255ee8033b17ace4b7755d40c19600a40abfa3409d1a353c07b5e23a46d5ac","source":{"kind":"arxiv","id":"1905.04425","version":1},"attestation_state":"computed","paper":{"title":"Cyclone intensity estimate with context-aware cyclegan","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Haitao Yang, Mingfei Cheng, Si Li, Yajing Xu","submitted_at":"2019-05-11T02:37:34Z","abstract_excerpt":"Deep learning approaches to cyclone intensity estimationhave recently shown promising results. However, sufferingfrom the extreme scarcity of cyclone data on specific in-tensity, most existing deep learning methods fail to achievesatisfactory performance on cyclone intensity estimation,especially on classes with few instances. To avoid the degra-dation of recognition performance caused by scarce samples,we propose a context-aware CycleGAN which learns the la-tent evolution features from adjacent cyclone intensity andsynthesizes CNN features of classes lacking samples fromunpaired source classe"},"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":"1905.04425","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-11T02:37:34Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"20a20434ceb511d026d68479d80514b96584f7c6984af86b244ef7cc0275fe05","abstract_canon_sha256":"73faebf360ec02997620bfc600d945aa65779d3814ac35ed9c29f29b3a9548e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:24.944166Z","signature_b64":"XQTj77bUo/KyuxINF2IGsiJhLmviI6f/ts1zFbN8HHPfzRIqz03mC+MQn/yQlGqYRejcnzOHva9cMeC1C65zDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af255ee8033b17ace4b7755d40c19600a40abfa3409d1a353c07b5e23a46d5ac","last_reissued_at":"2026-05-17T23:46:24.943295Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:24.943295Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cyclone intensity estimate with context-aware cyclegan","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Haitao Yang, Mingfei Cheng, Si Li, Yajing Xu","submitted_at":"2019-05-11T02:37:34Z","abstract_excerpt":"Deep learning approaches to cyclone intensity estimationhave recently shown promising results. However, sufferingfrom the extreme scarcity of cyclone data on specific in-tensity, most existing deep learning methods fail to achievesatisfactory performance on cyclone intensity estimation,especially on classes with few instances. To avoid the degra-dation of recognition performance caused by scarce samples,we propose a context-aware CycleGAN which learns the la-tent evolution features from adjacent cyclone intensity andsynthesizes CNN features of classes lacking samples fromunpaired source classe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.04425","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1905.04425","created_at":"2026-05-17T23:46:24.943558+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.04425v1","created_at":"2026-05-17T23:46:24.943558+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.04425","created_at":"2026-05-17T23:46:24.943558+00:00"},{"alias_kind":"pith_short_12","alias_value":"V4SV52ADHML2","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"V4SV52ADHML2ZZFX","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"V4SV52AD","created_at":"2026-05-18T12:33:30.264802+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/V4SV52ADHML2ZZFXOVOUBQMWAC","json":"https://pith.science/pith/V4SV52ADHML2ZZFXOVOUBQMWAC.json","graph_json":"https://pith.science/api/pith-number/V4SV52ADHML2ZZFXOVOUBQMWAC/graph.json","events_json":"https://pith.science/api/pith-number/V4SV52ADHML2ZZFXOVOUBQMWAC/events.json","paper":"https://pith.science/paper/V4SV52AD"},"agent_actions":{"view_html":"https://pith.science/pith/V4SV52ADHML2ZZFXOVOUBQMWAC","download_json":"https://pith.science/pith/V4SV52ADHML2ZZFXOVOUBQMWAC.json","view_paper":"https://pith.science/paper/V4SV52AD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.04425&json=true","fetch_graph":"https://pith.science/api/pith-number/V4SV52ADHML2ZZFXOVOUBQMWAC/graph.json","fetch_events":"https://pith.science/api/pith-number/V4SV52ADHML2ZZFXOVOUBQMWAC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V4SV52ADHML2ZZFXOVOUBQMWAC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V4SV52ADHML2ZZFXOVOUBQMWAC/action/storage_attestation","attest_author":"https://pith.science/pith/V4SV52ADHML2ZZFXOVOUBQMWAC/action/author_attestation","sign_citation":"https://pith.science/pith/V4SV52ADHML2ZZFXOVOUBQMWAC/action/citation_signature","submit_replication":"https://pith.science/pith/V4SV52ADHML2ZZFXOVOUBQMWAC/action/replication_record"}},"created_at":"2026-05-17T23:46:24.943558+00:00","updated_at":"2026-05-17T23:46:24.943558+00:00"}