{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:7OT75GMHMIF2AELM2VISBNOADO","short_pith_number":"pith:7OT75GMH","schema_version":"1.0","canonical_sha256":"fba7fe9987620ba0116cd55120b5c01bb7ebd57b7a115c8ba7bb632e6198dd21","source":{"kind":"arxiv","id":"1712.05042","version":2},"attestation_state":"computed","paper":{"title":"A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Bing Xue, Gary G. Yen, Mengjie Zhang, Yanan Sun","submitted_at":"2017-12-13T23:20:54Z","abstract_excerpt":"Convolutional auto-encoders have shown their remarkable performance in stacking to deep convolutional neural networks for classifying image data during past several years. However, they are unable to construct the state-of-the-art convolutional neural networks due to their intrinsic architectures. In this regard, we propose a flexible convolutional auto-encoder by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional convolutional auto-encoder. We also design an architecture discovery method by using particle swarm optimization, which is cap"},"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":"1712.05042","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-12-13T23:20:54Z","cross_cats_sorted":[],"title_canon_sha256":"ffc0bff507f7c3e287bd4537b825c02600957a282d878e7be2100e26be343e78","abstract_canon_sha256":"6ebe63b9de7a4d19560bf2465f43bc166a510298e9a322ff6ad6a3f29679fa1b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:09.998898Z","signature_b64":"VbsTCkVv1foGsTbLNpBEdfMBSvnLxwwnEAfS71KwwGZgMAl2VFdP4G5yR0NvWkF3QaXTu5abvbW1QE0tXRqKDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fba7fe9987620ba0116cd55120b5c01bb7ebd57b7a115c8ba7bb632e6198dd21","last_reissued_at":"2026-05-18T00:01:09.998229Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:09.998229Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Bing Xue, Gary G. Yen, Mengjie Zhang, Yanan Sun","submitted_at":"2017-12-13T23:20:54Z","abstract_excerpt":"Convolutional auto-encoders have shown their remarkable performance in stacking to deep convolutional neural networks for classifying image data during past several years. However, they are unable to construct the state-of-the-art convolutional neural networks due to their intrinsic architectures. In this regard, we propose a flexible convolutional auto-encoder by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional convolutional auto-encoder. We also design an architecture discovery method by using particle swarm optimization, which is cap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.05042","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1712.05042","created_at":"2026-05-18T00:01:09.998333+00:00"},{"alias_kind":"arxiv_version","alias_value":"1712.05042v2","created_at":"2026-05-18T00:01:09.998333+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.05042","created_at":"2026-05-18T00:01:09.998333+00:00"},{"alias_kind":"pith_short_12","alias_value":"7OT75GMHMIF2","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_16","alias_value":"7OT75GMHMIF2AELM","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_8","alias_value":"7OT75GMH","created_at":"2026-05-18T12:31:05.417338+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/7OT75GMHMIF2AELM2VISBNOADO","json":"https://pith.science/pith/7OT75GMHMIF2AELM2VISBNOADO.json","graph_json":"https://pith.science/api/pith-number/7OT75GMHMIF2AELM2VISBNOADO/graph.json","events_json":"https://pith.science/api/pith-number/7OT75GMHMIF2AELM2VISBNOADO/events.json","paper":"https://pith.science/paper/7OT75GMH"},"agent_actions":{"view_html":"https://pith.science/pith/7OT75GMHMIF2AELM2VISBNOADO","download_json":"https://pith.science/pith/7OT75GMHMIF2AELM2VISBNOADO.json","view_paper":"https://pith.science/paper/7OT75GMH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1712.05042&json=true","fetch_graph":"https://pith.science/api/pith-number/7OT75GMHMIF2AELM2VISBNOADO/graph.json","fetch_events":"https://pith.science/api/pith-number/7OT75GMHMIF2AELM2VISBNOADO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7OT75GMHMIF2AELM2VISBNOADO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7OT75GMHMIF2AELM2VISBNOADO/action/storage_attestation","attest_author":"https://pith.science/pith/7OT75GMHMIF2AELM2VISBNOADO/action/author_attestation","sign_citation":"https://pith.science/pith/7OT75GMHMIF2AELM2VISBNOADO/action/citation_signature","submit_replication":"https://pith.science/pith/7OT75GMHMIF2AELM2VISBNOADO/action/replication_record"}},"created_at":"2026-05-18T00:01:09.998333+00:00","updated_at":"2026-05-18T00:01:09.998333+00:00"}