{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:ZQM7DLUESG3DZAWAPU4CO6I44F","short_pith_number":"pith:ZQM7DLUE","schema_version":"1.0","canonical_sha256":"cc19f1ae8491b63c82c07d3827791ce15855c9b19ae5a1b568faf988b4feb6c4","source":{"kind":"arxiv","id":"2103.05236","version":2},"attestation_state":"computed","paper":{"title":"GAN Vocoder: Multi-Resolution Discriminator Is All You Need","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Dalhyun Kim, Geumbyeol Hwang, Gyeongsu Chae, Gyuhyeon Nam, Jaeseong You","submitted_at":"2021-03-09T05:47:43Z","abstract_excerpt":"Several of the latest GAN-based vocoders show remarkable achievements, outperforming autoregressive and flow-based competitors in both qualitative and quantitative measures while synthesizing orders of magnitude faster. In this work, we hypothesize that the common factor underlying their success is the multi-resolution discriminating framework, not the minute details in architecture, loss function, or training strategy. We experimentally test the hypothesis by evaluating six different generators paired with one shared multi-resolution discriminating framework. For all evaluative measures 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":"2103.05236","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2021-03-09T05:47:43Z","cross_cats_sorted":["cs.LG","eess.AS"],"title_canon_sha256":"59e975e146924ff3bb8f26a172717e79e462f67e2d871ed904f843a342128829","abstract_canon_sha256":"638acebeede9525711edb3f3a8bdd3b9b86633bdb373b7893b911b709cbbdcda"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:07:47.383895Z","signature_b64":"rNBh1KqnhpoB9yQiMK5gG/T/OSnnWeNtWla5/epQtDYI0Y04VZjuSu9mK6zntyJ2vPrjvYbKO0CT/cbBgKcnDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc19f1ae8491b63c82c07d3827791ce15855c9b19ae5a1b568faf988b4feb6c4","last_reissued_at":"2026-07-05T03:07:47.383487Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:07:47.383487Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GAN Vocoder: Multi-Resolution Discriminator Is All You Need","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Dalhyun Kim, Geumbyeol Hwang, Gyeongsu Chae, Gyuhyeon Nam, Jaeseong You","submitted_at":"2021-03-09T05:47:43Z","abstract_excerpt":"Several of the latest GAN-based vocoders show remarkable achievements, outperforming autoregressive and flow-based competitors in both qualitative and quantitative measures while synthesizing orders of magnitude faster. In this work, we hypothesize that the common factor underlying their success is the multi-resolution discriminating framework, not the minute details in architecture, loss function, or training strategy. We experimentally test the hypothesis by evaluating six different generators paired with one shared multi-resolution discriminating framework. For all evaluative measures with "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.05236","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/2103.05236/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":"2103.05236","created_at":"2026-07-05T03:07:47.383546+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.05236v2","created_at":"2026-07-05T03:07:47.383546+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.05236","created_at":"2026-07-05T03:07:47.383546+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZQM7DLUESG3D","created_at":"2026-07-05T03:07:47.383546+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZQM7DLUESG3DZAWA","created_at":"2026-07-05T03:07:47.383546+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZQM7DLUE","created_at":"2026-07-05T03:07:47.383546+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2210.13438","citing_title":"High Fidelity Neural Audio Compression","ref_index":34,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZQM7DLUESG3DZAWAPU4CO6I44F","json":"https://pith.science/pith/ZQM7DLUESG3DZAWAPU4CO6I44F.json","graph_json":"https://pith.science/api/pith-number/ZQM7DLUESG3DZAWAPU4CO6I44F/graph.json","events_json":"https://pith.science/api/pith-number/ZQM7DLUESG3DZAWAPU4CO6I44F/events.json","paper":"https://pith.science/paper/ZQM7DLUE"},"agent_actions":{"view_html":"https://pith.science/pith/ZQM7DLUESG3DZAWAPU4CO6I44F","download_json":"https://pith.science/pith/ZQM7DLUESG3DZAWAPU4CO6I44F.json","view_paper":"https://pith.science/paper/ZQM7DLUE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.05236&json=true","fetch_graph":"https://pith.science/api/pith-number/ZQM7DLUESG3DZAWAPU4CO6I44F/graph.json","fetch_events":"https://pith.science/api/pith-number/ZQM7DLUESG3DZAWAPU4CO6I44F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZQM7DLUESG3DZAWAPU4CO6I44F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZQM7DLUESG3DZAWAPU4CO6I44F/action/storage_attestation","attest_author":"https://pith.science/pith/ZQM7DLUESG3DZAWAPU4CO6I44F/action/author_attestation","sign_citation":"https://pith.science/pith/ZQM7DLUESG3DZAWAPU4CO6I44F/action/citation_signature","submit_replication":"https://pith.science/pith/ZQM7DLUESG3DZAWAPU4CO6I44F/action/replication_record"}},"created_at":"2026-07-05T03:07:47.383546+00:00","updated_at":"2026-07-05T03:07:47.383546+00:00"}