{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:3KEEDG7652JF45WHHNHE75UKIL","short_pith_number":"pith:3KEEDG76","canonical_record":{"source":{"id":"2106.07631","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-14T17:39:49Z","cross_cats_sorted":[],"title_canon_sha256":"61efc5e0a764c65ef2a96440677c7a5d4966f782c9436fd871b3a04218172b7e","abstract_canon_sha256":"5425e5d5fcc2e3fc030cebc8ebf20dd5e9ec16a187b1b4be63b56fed0a738d08"},"schema_version":"1.0"},"canonical_sha256":"da88419bfeee925e76c73b4e4ff68a42cdba982eb096fe7940584170301e6356","source":{"kind":"arxiv","id":"2106.07631","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.07631","created_at":"2026-07-05T03:43:42Z"},{"alias_kind":"arxiv_version","alias_value":"2106.07631v3","created_at":"2026-07-05T03:43:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.07631","created_at":"2026-07-05T03:43:42Z"},{"alias_kind":"pith_short_12","alias_value":"3KEEDG7652JF","created_at":"2026-07-05T03:43:42Z"},{"alias_kind":"pith_short_16","alias_value":"3KEEDG7652JF45WH","created_at":"2026-07-05T03:43:42Z"},{"alias_kind":"pith_short_8","alias_value":"3KEEDG76","created_at":"2026-07-05T03:43:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:3KEEDG7652JF45WHHNHE75UKIL","target":"record","payload":{"canonical_record":{"source":{"id":"2106.07631","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-14T17:39:49Z","cross_cats_sorted":[],"title_canon_sha256":"61efc5e0a764c65ef2a96440677c7a5d4966f782c9436fd871b3a04218172b7e","abstract_canon_sha256":"5425e5d5fcc2e3fc030cebc8ebf20dd5e9ec16a187b1b4be63b56fed0a738d08"},"schema_version":"1.0"},"canonical_sha256":"da88419bfeee925e76c73b4e4ff68a42cdba982eb096fe7940584170301e6356","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:43:42.023903Z","signature_b64":"uKyorgVnf9KAVduickQ1gjR86t8wBmC0dxq8czrpNYpth6mJLMunGuGViJ5snX4kG9aS0XmQ7yVCXVW6QKptBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da88419bfeee925e76c73b4e4ff68a42cdba982eb096fe7940584170301e6356","last_reissued_at":"2026-07-05T03:43:42.023468Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:43:42.023468Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2106.07631","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:43:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LH4lN17B6EKNlBEeGHDeuo+tO6ffOn/LPtEDCkbQofTVIKVcw4twjgHfJScZZHOD3b8Elsn3p/nIaKWhnCLTAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-20T08:59:05.677804Z"},"content_sha256":"f0c7aa2dfbc13a531078ffe196d8cd6f47490cf88db6f2e8f573719cbc9b3e3e","schema_version":"1.0","event_id":"sha256:f0c7aa2dfbc13a531078ffe196d8cd6f47490cf88db6f2e8f573719cbc9b3e3e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:3KEEDG7652JF45WHHNHE75UKIL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improved Transformer for High-Resolution GANs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dimitris N. Metaxas, Han Zhang, Long Zhao, Ting Chen, Zizhao Zhang","submitted_at":"2021-06-14T17:39:49Z","abstract_excerpt":"Attention-based models, exemplified by the Transformer, can effectively model long range dependency, but suffer from the quadratic complexity of self-attention operation, making them difficult to be adopted for high-resolution image generation based on Generative Adversarial Networks (GANs). In this paper, we introduce two key ingredients to Transformer to address this challenge. First, in low-resolution stages of the generative process, standard global self-attention is replaced with the proposed multi-axis blocked self-attention which allows efficient mixing of local and global attention. Se"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.07631","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2106.07631/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:43:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LmFZNb4Z6Y5hmS1n0aaX9nyc1VOBUl3RMNnjXHqARq0CbG1jYqVmouLKMYH+w7U3qNpkK4kmmI0G573ZPpOTDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-20T08:59:05.678175Z"},"content_sha256":"2346610d51b71b0b9c85a966803386c2aca9208774626903ebaf6d2a38824efe","schema_version":"1.0","event_id":"sha256:2346610d51b71b0b9c85a966803386c2aca9208774626903ebaf6d2a38824efe"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3KEEDG7652JF45WHHNHE75UKIL/bundle.json","state_url":"https://pith.science/pith/3KEEDG7652JF45WHHNHE75UKIL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3KEEDG7652JF45WHHNHE75UKIL/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-20T08:59:05Z","links":{"resolver":"https://pith.science/pith/3KEEDG7652JF45WHHNHE75UKIL","bundle":"https://pith.science/pith/3KEEDG7652JF45WHHNHE75UKIL/bundle.json","state":"https://pith.science/pith/3KEEDG7652JF45WHHNHE75UKIL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3KEEDG7652JF45WHHNHE75UKIL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:3KEEDG7652JF45WHHNHE75UKIL","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"5425e5d5fcc2e3fc030cebc8ebf20dd5e9ec16a187b1b4be63b56fed0a738d08","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-14T17:39:49Z","title_canon_sha256":"61efc5e0a764c65ef2a96440677c7a5d4966f782c9436fd871b3a04218172b7e"},"schema_version":"1.0","source":{"id":"2106.07631","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.07631","created_at":"2026-07-05T03:43:42Z"},{"alias_kind":"arxiv_version","alias_value":"2106.07631v3","created_at":"2026-07-05T03:43:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.07631","created_at":"2026-07-05T03:43:42Z"},{"alias_kind":"pith_short_12","alias_value":"3KEEDG7652JF","created_at":"2026-07-05T03:43:42Z"},{"alias_kind":"pith_short_16","alias_value":"3KEEDG7652JF45WH","created_at":"2026-07-05T03:43:42Z"},{"alias_kind":"pith_short_8","alias_value":"3KEEDG76","created_at":"2026-07-05T03:43:42Z"}],"graph_snapshots":[{"event_id":"sha256:2346610d51b71b0b9c85a966803386c2aca9208774626903ebaf6d2a38824efe","target":"graph","created_at":"2026-07-05T03:43:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2106.07631/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Attention-based models, exemplified by the Transformer, can effectively model long range dependency, but suffer from the quadratic complexity of self-attention operation, making them difficult to be adopted for high-resolution image generation based on Generative Adversarial Networks (GANs). In this paper, we introduce two key ingredients to Transformer to address this challenge. First, in low-resolution stages of the generative process, standard global self-attention is replaced with the proposed multi-axis blocked self-attention which allows efficient mixing of local and global attention. Se","authors_text":"Dimitris N. Metaxas, Han Zhang, Long Zhao, Ting Chen, Zizhao Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-14T17:39:49Z","title":"Improved Transformer for High-Resolution GANs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.07631","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f0c7aa2dfbc13a531078ffe196d8cd6f47490cf88db6f2e8f573719cbc9b3e3e","target":"record","created_at":"2026-07-05T03:43:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"5425e5d5fcc2e3fc030cebc8ebf20dd5e9ec16a187b1b4be63b56fed0a738d08","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-14T17:39:49Z","title_canon_sha256":"61efc5e0a764c65ef2a96440677c7a5d4966f782c9436fd871b3a04218172b7e"},"schema_version":"1.0","source":{"id":"2106.07631","kind":"arxiv","version":3}},"canonical_sha256":"da88419bfeee925e76c73b4e4ff68a42cdba982eb096fe7940584170301e6356","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"da88419bfeee925e76c73b4e4ff68a42cdba982eb096fe7940584170301e6356","first_computed_at":"2026-07-05T03:43:42.023468Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:43:42.023468Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uKyorgVnf9KAVduickQ1gjR86t8wBmC0dxq8czrpNYpth6mJLMunGuGViJ5snX4kG9aS0XmQ7yVCXVW6QKptBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:43:42.023903Z","signed_message":"canonical_sha256_bytes"},"source_id":"2106.07631","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f0c7aa2dfbc13a531078ffe196d8cd6f47490cf88db6f2e8f573719cbc9b3e3e","sha256:2346610d51b71b0b9c85a966803386c2aca9208774626903ebaf6d2a38824efe"],"state_sha256":"4c5271075f71a2f24c90c81c9acf6e086ffb33e4db36987576cc0cef3fa4eeb8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"89+uko6F+J2LohE8G3glmstLQ6N5+bt7I/LPRzJx6dCUElsHomKKEFwsah7MQPDrXdnCwmKX84sXP0opprkTDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-20T08:59:05.680282Z","bundle_sha256":"05847368838273b087b9bd19e4e35d3bdf7418eae283adbf015160f14a1d574d"}}