{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:YX2LWFP5BOIL7V35BHKDEOQJ64","short_pith_number":"pith:YX2LWFP5","schema_version":"1.0","canonical_sha256":"c5f4bb15fd0b90bfd77d09d4323a09f70c2d5bd61d3207cf95d726cd89cd8f51","source":{"kind":"arxiv","id":"2109.03124","version":2},"attestation_state":"computed","paper":{"title":"GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.LG","authors_text":"Sheng-hua Zhong, Yan Liu, Zhi Zhang","submitted_at":"2021-09-07T14:42:55Z","abstract_excerpt":"The data scarcity problem in Electroencephalography (EEG) based affective computing results into difficulty in building an effective model with high accuracy and stability using machine learning algorithms especially deep learning models. Data augmentation has recently achieved considerable performance improvement for deep learning models: increased accuracy, stability, and reduced over-fitting. In this paper, we propose a novel data augmentation framework, namely Generative Adversarial Network-based Self-supervised Data Augmentation (GANSER). As the first to combine adversarial training with "},"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":"2109.03124","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-09-07T14:42:55Z","cross_cats_sorted":["cs.AI","cs.HC"],"title_canon_sha256":"89db78446d558b7107d6c6ed13ba2288f70ff922d66c83a6f7a132ac5fa7e3ba","abstract_canon_sha256":"fbfb6b6e4a1dbdaedc8f7b0552b7f0e71842881254cabe702fbba3e1e22d8b83"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:12:29.001699Z","signature_b64":"aQC6JU7zvxrOdsI4fhDZs9OybIlXXalS3SfjURf48pqEJKi1zDohSHGvw1cE07NLtIKn96hEeW9WxBa+6Oi7Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5f4bb15fd0b90bfd77d09d4323a09f70c2d5bd61d3207cf95d726cd89cd8f51","last_reissued_at":"2026-07-05T03:12:29.001341Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:12:29.001341Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.LG","authors_text":"Sheng-hua Zhong, Yan Liu, Zhi Zhang","submitted_at":"2021-09-07T14:42:55Z","abstract_excerpt":"The data scarcity problem in Electroencephalography (EEG) based affective computing results into difficulty in building an effective model with high accuracy and stability using machine learning algorithms especially deep learning models. Data augmentation has recently achieved considerable performance improvement for deep learning models: increased accuracy, stability, and reduced over-fitting. In this paper, we propose a novel data augmentation framework, namely Generative Adversarial Network-based Self-supervised Data Augmentation (GANSER). As the first to combine adversarial training with "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.03124","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2109.03124/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":"2109.03124","created_at":"2026-07-05T03:12:29.001399+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.03124v2","created_at":"2026-07-05T03:12:29.001399+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.03124","created_at":"2026-07-05T03:12:29.001399+00:00"},{"alias_kind":"pith_short_12","alias_value":"YX2LWFP5BOIL","created_at":"2026-07-05T03:12:29.001399+00:00"},{"alias_kind":"pith_short_16","alias_value":"YX2LWFP5BOIL7V35","created_at":"2026-07-05T03:12:29.001399+00:00"},{"alias_kind":"pith_short_8","alias_value":"YX2LWFP5","created_at":"2026-07-05T03:12:29.001399+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.05931","citing_title":"Protecting Intellectual Property of EEG-based Neural Networks with Watermarking","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YX2LWFP5BOIL7V35BHKDEOQJ64","json":"https://pith.science/pith/YX2LWFP5BOIL7V35BHKDEOQJ64.json","graph_json":"https://pith.science/api/pith-number/YX2LWFP5BOIL7V35BHKDEOQJ64/graph.json","events_json":"https://pith.science/api/pith-number/YX2LWFP5BOIL7V35BHKDEOQJ64/events.json","paper":"https://pith.science/paper/YX2LWFP5"},"agent_actions":{"view_html":"https://pith.science/pith/YX2LWFP5BOIL7V35BHKDEOQJ64","download_json":"https://pith.science/pith/YX2LWFP5BOIL7V35BHKDEOQJ64.json","view_paper":"https://pith.science/paper/YX2LWFP5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.03124&json=true","fetch_graph":"https://pith.science/api/pith-number/YX2LWFP5BOIL7V35BHKDEOQJ64/graph.json","fetch_events":"https://pith.science/api/pith-number/YX2LWFP5BOIL7V35BHKDEOQJ64/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YX2LWFP5BOIL7V35BHKDEOQJ64/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YX2LWFP5BOIL7V35BHKDEOQJ64/action/storage_attestation","attest_author":"https://pith.science/pith/YX2LWFP5BOIL7V35BHKDEOQJ64/action/author_attestation","sign_citation":"https://pith.science/pith/YX2LWFP5BOIL7V35BHKDEOQJ64/action/citation_signature","submit_replication":"https://pith.science/pith/YX2LWFP5BOIL7V35BHKDEOQJ64/action/replication_record"}},"created_at":"2026-07-05T03:12:29.001399+00:00","updated_at":"2026-07-05T03:12:29.001399+00:00"}