{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:COEWRTH63BYHGZLJ5ILSDLDBRO","short_pith_number":"pith:COEWRTH6","schema_version":"1.0","canonical_sha256":"138968ccfed870736569ea1721ac618baa5837d407adc9cf38a68da25aa213d4","source":{"kind":"arxiv","id":"1710.03144","version":3},"attestation_state":"computed","paper":{"title":"Island Loss for Learning Discriminative Features in Facial Expression Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ahmed Shehab Khan, James O'Reilly, Jie Cai, Yan Tong, Zhiyuan Li, Zibo Meng","submitted_at":"2017-10-09T15:26:22Z","abstract_excerpt":"Over the past few years, Convolutional Neural Networks (CNNs) have shown promise on facial expression recognition. However, the performance degrades dramatically under real-world settings due to variations introduced by subtle facial appearance changes, head pose variations, illumination changes, and occlusions.\n  In this paper, a novel island loss is proposed to enhance the discriminative power of the deeply learned features. Specifically, the IL is designed to reduce the intra-class variations while enlarging the inter-class differences simultaneously. Experimental results on four benchmark "},"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":"1710.03144","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-09T15:26:22Z","cross_cats_sorted":[],"title_canon_sha256":"1dd9e8ab53a07b99a482a5549168f8c5b73e8cd74ee5024cb6e00ea98193f657","abstract_canon_sha256":"597ae27e2c7642dbc6def5dcc7f82e4ffb5e42e32b68c79887e8d9d07481f284"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:25:07.437226Z","signature_b64":"kppFcxrEUq2jjMrUlmyvSPiJ2mw/GgAv/I5yO5ii6gPtjVgYNQu+f8YpRl/vknAm6xQe2MOZ7hL4L4+G9aFmAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"138968ccfed870736569ea1721ac618baa5837d407adc9cf38a68da25aa213d4","last_reissued_at":"2026-05-18T00:25:07.436580Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:25:07.436580Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Island Loss for Learning Discriminative Features in Facial Expression Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ahmed Shehab Khan, James O'Reilly, Jie Cai, Yan Tong, Zhiyuan Li, Zibo Meng","submitted_at":"2017-10-09T15:26:22Z","abstract_excerpt":"Over the past few years, Convolutional Neural Networks (CNNs) have shown promise on facial expression recognition. However, the performance degrades dramatically under real-world settings due to variations introduced by subtle facial appearance changes, head pose variations, illumination changes, and occlusions.\n  In this paper, a novel island loss is proposed to enhance the discriminative power of the deeply learned features. Specifically, the IL is designed to reduce the intra-class variations while enlarging the inter-class differences simultaneously. Experimental results on four benchmark "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.03144","kind":"arxiv","version":3},"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":"1710.03144","created_at":"2026-05-18T00:25:07.436679+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.03144v3","created_at":"2026-05-18T00:25:07.436679+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.03144","created_at":"2026-05-18T00:25:07.436679+00:00"},{"alias_kind":"pith_short_12","alias_value":"COEWRTH63BYH","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_16","alias_value":"COEWRTH63BYHGZLJ","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_8","alias_value":"COEWRTH6","created_at":"2026-05-18T12:31:10.602751+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/COEWRTH63BYHGZLJ5ILSDLDBRO","json":"https://pith.science/pith/COEWRTH63BYHGZLJ5ILSDLDBRO.json","graph_json":"https://pith.science/api/pith-number/COEWRTH63BYHGZLJ5ILSDLDBRO/graph.json","events_json":"https://pith.science/api/pith-number/COEWRTH63BYHGZLJ5ILSDLDBRO/events.json","paper":"https://pith.science/paper/COEWRTH6"},"agent_actions":{"view_html":"https://pith.science/pith/COEWRTH63BYHGZLJ5ILSDLDBRO","download_json":"https://pith.science/pith/COEWRTH63BYHGZLJ5ILSDLDBRO.json","view_paper":"https://pith.science/paper/COEWRTH6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.03144&json=true","fetch_graph":"https://pith.science/api/pith-number/COEWRTH63BYHGZLJ5ILSDLDBRO/graph.json","fetch_events":"https://pith.science/api/pith-number/COEWRTH63BYHGZLJ5ILSDLDBRO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/COEWRTH63BYHGZLJ5ILSDLDBRO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/COEWRTH63BYHGZLJ5ILSDLDBRO/action/storage_attestation","attest_author":"https://pith.science/pith/COEWRTH63BYHGZLJ5ILSDLDBRO/action/author_attestation","sign_citation":"https://pith.science/pith/COEWRTH63BYHGZLJ5ILSDLDBRO/action/citation_signature","submit_replication":"https://pith.science/pith/COEWRTH63BYHGZLJ5ILSDLDBRO/action/replication_record"}},"created_at":"2026-05-18T00:25:07.436679+00:00","updated_at":"2026-05-18T00:25:07.436679+00:00"}