{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:CZPYQP2ENOLDJDNKWJ4RULRJU7","short_pith_number":"pith:CZPYQP2E","schema_version":"1.0","canonical_sha256":"165f883f446b96348daab2791a2e29a7ca635e8fcff8ad43ef8f8a2f3c2429d3","source":{"kind":"arxiv","id":"2112.10762","version":2},"attestation_state":"computed","paper":{"title":"StyleSwin: Transformer-based GAN for High-resolution Image Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Baining Guo, Bowen Zhang, Bo Zhang, Dong Chen, Fang Wen, Jianmin Bao, Shuyang Gu, Yong Wang","submitted_at":"2021-12-20T18:59:51Z","abstract_excerpt":"Despite the tantalizing success in a broad of vision tasks, transformers have not yet demonstrated on-par ability as ConvNets in high-resolution image generative modeling. In this paper, we seek to explore using pure transformers to build a generative adversarial network for high-resolution image synthesis. To this end, we believe that local attention is crucial to strike the balance between computational efficiency and modeling capacity. Hence, the proposed generator adopts Swin transformer in a style-based architecture. To achieve a larger receptive field, we propose double attention which s"},"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":"2112.10762","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-12-20T18:59:51Z","cross_cats_sorted":[],"title_canon_sha256":"733be8a5a8b7df1b32cf5cba69d28f3731951dfdd0a4b6d64b7b4fad6858ab09","abstract_canon_sha256":"72004af79c2aedd31972e393fba2b21a8967d24652493d9f6457d31b0bc0fdbf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:42:16.009056Z","signature_b64":"xxxYsfgMxxn4z2HxAEjdICUOk6FNsXEd62Nm9NDSxtEtT32mAeFCSQ+cdfNZqEaPt7empf4CZZoaq+KVYTiUAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"165f883f446b96348daab2791a2e29a7ca635e8fcff8ad43ef8f8a2f3c2429d3","last_reissued_at":"2026-07-05T04:42:16.008586Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:42:16.008586Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"StyleSwin: Transformer-based GAN for High-resolution Image Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Baining Guo, Bowen Zhang, Bo Zhang, Dong Chen, Fang Wen, Jianmin Bao, Shuyang Gu, Yong Wang","submitted_at":"2021-12-20T18:59:51Z","abstract_excerpt":"Despite the tantalizing success in a broad of vision tasks, transformers have not yet demonstrated on-par ability as ConvNets in high-resolution image generative modeling. In this paper, we seek to explore using pure transformers to build a generative adversarial network for high-resolution image synthesis. To this end, we believe that local attention is crucial to strike the balance between computational efficiency and modeling capacity. Hence, the proposed generator adopts Swin transformer in a style-based architecture. To achieve a larger receptive field, we propose double attention which s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.10762","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/2112.10762/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":"2112.10762","created_at":"2026-07-05T04:42:16.008642+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.10762v2","created_at":"2026-07-05T04:42:16.008642+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.10762","created_at":"2026-07-05T04:42:16.008642+00:00"},{"alias_kind":"pith_short_12","alias_value":"CZPYQP2ENOLD","created_at":"2026-07-05T04:42:16.008642+00:00"},{"alias_kind":"pith_short_16","alias_value":"CZPYQP2ENOLDJDNK","created_at":"2026-07-05T04:42:16.008642+00:00"},{"alias_kind":"pith_short_8","alias_value":"CZPYQP2E","created_at":"2026-07-05T04:42:16.008642+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/CZPYQP2ENOLDJDNKWJ4RULRJU7","json":"https://pith.science/pith/CZPYQP2ENOLDJDNKWJ4RULRJU7.json","graph_json":"https://pith.science/api/pith-number/CZPYQP2ENOLDJDNKWJ4RULRJU7/graph.json","events_json":"https://pith.science/api/pith-number/CZPYQP2ENOLDJDNKWJ4RULRJU7/events.json","paper":"https://pith.science/paper/CZPYQP2E"},"agent_actions":{"view_html":"https://pith.science/pith/CZPYQP2ENOLDJDNKWJ4RULRJU7","download_json":"https://pith.science/pith/CZPYQP2ENOLDJDNKWJ4RULRJU7.json","view_paper":"https://pith.science/paper/CZPYQP2E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.10762&json=true","fetch_graph":"https://pith.science/api/pith-number/CZPYQP2ENOLDJDNKWJ4RULRJU7/graph.json","fetch_events":"https://pith.science/api/pith-number/CZPYQP2ENOLDJDNKWJ4RULRJU7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CZPYQP2ENOLDJDNKWJ4RULRJU7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CZPYQP2ENOLDJDNKWJ4RULRJU7/action/storage_attestation","attest_author":"https://pith.science/pith/CZPYQP2ENOLDJDNKWJ4RULRJU7/action/author_attestation","sign_citation":"https://pith.science/pith/CZPYQP2ENOLDJDNKWJ4RULRJU7/action/citation_signature","submit_replication":"https://pith.science/pith/CZPYQP2ENOLDJDNKWJ4RULRJU7/action/replication_record"}},"created_at":"2026-07-05T04:42:16.008642+00:00","updated_at":"2026-07-05T04:42:16.008642+00:00"}