{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EUVPGDNTLTP37DKPYYGOFVEYFJ","short_pith_number":"pith:EUVPGDNT","schema_version":"1.0","canonical_sha256":"252af30db35cdfbf8d4fc60ce2d4982a66a72b517c4f5554d70743954e64a64b","source":{"kind":"arxiv","id":"2409.10445","version":1},"attestation_state":"computed","paper":{"title":"Deep-Wide Learning Assistance for Insect Pest Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Binh Nguyen, Hieu Ung, Huy Nguyen, Huy Ung, Toan Nguyen","submitted_at":"2024-09-16T16:29:41Z","abstract_excerpt":"Accurate insect pest recognition plays a critical role in agriculture. It is a challenging problem due to the intricate characteristics of insects. In this paper, we present DeWi, novel learning assistance for insect pest classification. With a one-stage and alternating training strategy, DeWi simultaneously improves several Convolutional Neural Networks in two perspectives: discrimination (by optimizing a triplet margin loss in a supervised training manner) and generalization (via data augmentation). From that, DeWi can learn discriminative and in-depth features of insect pests (deep) yet sti"},"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":"2409.10445","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-16T16:29:41Z","cross_cats_sorted":[],"title_canon_sha256":"0cf030058d33963d5c553cf6946e5b05bb43082080c9a5906ecd22e6ca8a73a3","abstract_canon_sha256":"b0907073188837e69d0e2c6ec416aabcd6d4ee17cf76205fe448186b89405903"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:07:40.192628Z","signature_b64":"q2U8zL16fMxsh4DAQtgAhZoPjz+znxIBhAjF/Fpq/6POE+ljdj+sOUcSKD6DYOgaRzF5kpEuETpPK+JgPFo9Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"252af30db35cdfbf8d4fc60ce2d4982a66a72b517c4f5554d70743954e64a64b","last_reissued_at":"2026-07-05T09:07:40.192113Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:07:40.192113Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep-Wide Learning Assistance for Insect Pest Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Binh Nguyen, Hieu Ung, Huy Nguyen, Huy Ung, Toan Nguyen","submitted_at":"2024-09-16T16:29:41Z","abstract_excerpt":"Accurate insect pest recognition plays a critical role in agriculture. It is a challenging problem due to the intricate characteristics of insects. In this paper, we present DeWi, novel learning assistance for insect pest classification. With a one-stage and alternating training strategy, DeWi simultaneously improves several Convolutional Neural Networks in two perspectives: discrimination (by optimizing a triplet margin loss in a supervised training manner) and generalization (via data augmentation). From that, DeWi can learn discriminative and in-depth features of insect pests (deep) yet sti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.10445","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2409.10445/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":"2409.10445","created_at":"2026-07-05T09:07:40.192175+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.10445v1","created_at":"2026-07-05T09:07:40.192175+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.10445","created_at":"2026-07-05T09:07:40.192175+00:00"},{"alias_kind":"pith_short_12","alias_value":"EUVPGDNTLTP3","created_at":"2026-07-05T09:07:40.192175+00:00"},{"alias_kind":"pith_short_16","alias_value":"EUVPGDNTLTP37DKP","created_at":"2026-07-05T09:07:40.192175+00:00"},{"alias_kind":"pith_short_8","alias_value":"EUVPGDNT","created_at":"2026-07-05T09:07:40.192175+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/EUVPGDNTLTP37DKPYYGOFVEYFJ","json":"https://pith.science/pith/EUVPGDNTLTP37DKPYYGOFVEYFJ.json","graph_json":"https://pith.science/api/pith-number/EUVPGDNTLTP37DKPYYGOFVEYFJ/graph.json","events_json":"https://pith.science/api/pith-number/EUVPGDNTLTP37DKPYYGOFVEYFJ/events.json","paper":"https://pith.science/paper/EUVPGDNT"},"agent_actions":{"view_html":"https://pith.science/pith/EUVPGDNTLTP37DKPYYGOFVEYFJ","download_json":"https://pith.science/pith/EUVPGDNTLTP37DKPYYGOFVEYFJ.json","view_paper":"https://pith.science/paper/EUVPGDNT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.10445&json=true","fetch_graph":"https://pith.science/api/pith-number/EUVPGDNTLTP37DKPYYGOFVEYFJ/graph.json","fetch_events":"https://pith.science/api/pith-number/EUVPGDNTLTP37DKPYYGOFVEYFJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EUVPGDNTLTP37DKPYYGOFVEYFJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EUVPGDNTLTP37DKPYYGOFVEYFJ/action/storage_attestation","attest_author":"https://pith.science/pith/EUVPGDNTLTP37DKPYYGOFVEYFJ/action/author_attestation","sign_citation":"https://pith.science/pith/EUVPGDNTLTP37DKPYYGOFVEYFJ/action/citation_signature","submit_replication":"https://pith.science/pith/EUVPGDNTLTP37DKPYYGOFVEYFJ/action/replication_record"}},"created_at":"2026-07-05T09:07:40.192175+00:00","updated_at":"2026-07-05T09:07:40.192175+00:00"}