{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:FGYAZSIJXDHOPLNS4IJZ2PAA45","short_pith_number":"pith:FGYAZSIJ","canonical_record":{"source":{"id":"2504.13622","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-04-18T10:55:24Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"825bb73f3dd39f3cae2e2b9efcf5dfbde4e2b22a1b5cb6560f0dd2035a47d83f","abstract_canon_sha256":"a71c8ac572db677f6dbc9ff9f064d6568173fb19787e5a723aca1bd0be655609"},"schema_version":"1.0"},"canonical_sha256":"29b00cc909b8cee7adb2e2139d3c00e760aff754039a6296bac56a350481bd55","source":{"kind":"arxiv","id":"2504.13622","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.13622","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"arxiv_version","alias_value":"2504.13622v1","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.13622","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"pith_short_12","alias_value":"FGYAZSIJXDHO","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"pith_short_16","alias_value":"FGYAZSIJXDHOPLNS","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"pith_short_8","alias_value":"FGYAZSIJ","created_at":"2026-07-05T10:50:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:FGYAZSIJXDHOPLNS4IJZ2PAA45","target":"record","payload":{"canonical_record":{"source":{"id":"2504.13622","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-04-18T10:55:24Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"825bb73f3dd39f3cae2e2b9efcf5dfbde4e2b22a1b5cb6560f0dd2035a47d83f","abstract_canon_sha256":"a71c8ac572db677f6dbc9ff9f064d6568173fb19787e5a723aca1bd0be655609"},"schema_version":"1.0"},"canonical_sha256":"29b00cc909b8cee7adb2e2139d3c00e760aff754039a6296bac56a350481bd55","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:50:59.246067Z","signature_b64":"NVHDrKG/R/FzDIN43iS3PEdgKDjlXf3DhmeN7kGEX5y91KgEyt1h/mCIaYXQKp0DbVe+9oxW5ykChGJFHMZiBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"29b00cc909b8cee7adb2e2139d3c00e760aff754039a6296bac56a350481bd55","last_reissued_at":"2026-07-05T10:50:59.245573Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:50:59.245573Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.13622","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:50:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7dlZtumjNrHCaLRxB0e8/Ld0PpPRnK+3OBxgCTyg7PVqF3W85TmzzToF8l5XCw+uZzCVzCyp0+WBAiSPfa2/Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:43:18.033881Z"},"content_sha256":"e4309a9fab173a091539a7dc12ad1b803d1027bfacf3e5054a20eabeb326d3fb","schema_version":"1.0","event_id":"sha256:e4309a9fab173a091539a7dc12ad1b803d1027bfacf3e5054a20eabeb326d3fb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:FGYAZSIJXDHOPLNS4IJZ2PAA45","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SupResDiffGAN a new approach for the Super-Resolution task","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Dawid Kope\\'c, Dawid Krutul, Jan Koco\\'n, Maciej Wizerkaniuk, Maciej Zi\\k{e}ba, Wojciech Koz{\\l}owski","submitted_at":"2025-04-18T10:55:24Z","abstract_excerpt":"In this work, we present SupResDiffGAN, a novel hybrid architecture that combines the strengths of Generative Adversarial Networks (GANs) and diffusion models for super-resolution tasks. By leveraging latent space representations and reducing the number of diffusion steps, SupResDiffGAN achieves significantly faster inference times than other diffusion-based super-resolution models while maintaining competitive perceptual quality. To prevent discriminator overfitting, we propose adaptive noise corruption, ensuring a stable balance between the generator and the discriminator during training. Ex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.13622","kind":"arxiv","version":1},"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/2504.13622/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":"2026-07-05T10:50:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4VPptM86ZF7RVNncq/VkHVfLdBmcCqFP50gFBjhIHFEKzdRtrm1JAfckeVOC7M9Lfnd7/cPrbyg3pUWj06mACQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:43:18.034247Z"},"content_sha256":"2a600e3e5cbdc1bb87d6853d3c1f55fddbb8423a70f8832e191f8cc0b85625bd","schema_version":"1.0","event_id":"sha256:2a600e3e5cbdc1bb87d6853d3c1f55fddbb8423a70f8832e191f8cc0b85625bd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FGYAZSIJXDHOPLNS4IJZ2PAA45/bundle.json","state_url":"https://pith.science/pith/FGYAZSIJXDHOPLNS4IJZ2PAA45/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FGYAZSIJXDHOPLNS4IJZ2PAA45/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":"2026-07-07T15:43:18Z","links":{"resolver":"https://pith.science/pith/FGYAZSIJXDHOPLNS4IJZ2PAA45","bundle":"https://pith.science/pith/FGYAZSIJXDHOPLNS4IJZ2PAA45/bundle.json","state":"https://pith.science/pith/FGYAZSIJXDHOPLNS4IJZ2PAA45/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FGYAZSIJXDHOPLNS4IJZ2PAA45/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FGYAZSIJXDHOPLNS4IJZ2PAA45","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":"a71c8ac572db677f6dbc9ff9f064d6568173fb19787e5a723aca1bd0be655609","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-04-18T10:55:24Z","title_canon_sha256":"825bb73f3dd39f3cae2e2b9efcf5dfbde4e2b22a1b5cb6560f0dd2035a47d83f"},"schema_version":"1.0","source":{"id":"2504.13622","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.13622","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"arxiv_version","alias_value":"2504.13622v1","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.13622","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"pith_short_12","alias_value":"FGYAZSIJXDHO","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"pith_short_16","alias_value":"FGYAZSIJXDHOPLNS","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"pith_short_8","alias_value":"FGYAZSIJ","created_at":"2026-07-05T10:50:59Z"}],"graph_snapshots":[{"event_id":"sha256:2a600e3e5cbdc1bb87d6853d3c1f55fddbb8423a70f8832e191f8cc0b85625bd","target":"graph","created_at":"2026-07-05T10:50:59Z","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/2504.13622/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this work, we present SupResDiffGAN, a novel hybrid architecture that combines the strengths of Generative Adversarial Networks (GANs) and diffusion models for super-resolution tasks. By leveraging latent space representations and reducing the number of diffusion steps, SupResDiffGAN achieves significantly faster inference times than other diffusion-based super-resolution models while maintaining competitive perceptual quality. To prevent discriminator overfitting, we propose adaptive noise corruption, ensuring a stable balance between the generator and the discriminator during training. Ex","authors_text":"Dawid Kope\\'c, Dawid Krutul, Jan Koco\\'n, Maciej Wizerkaniuk, Maciej Zi\\k{e}ba, Wojciech Koz{\\l}owski","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-04-18T10:55:24Z","title":"SupResDiffGAN a new approach for the Super-Resolution task"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.13622","kind":"arxiv","version":1},"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:e4309a9fab173a091539a7dc12ad1b803d1027bfacf3e5054a20eabeb326d3fb","target":"record","created_at":"2026-07-05T10:50:59Z","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":"a71c8ac572db677f6dbc9ff9f064d6568173fb19787e5a723aca1bd0be655609","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-04-18T10:55:24Z","title_canon_sha256":"825bb73f3dd39f3cae2e2b9efcf5dfbde4e2b22a1b5cb6560f0dd2035a47d83f"},"schema_version":"1.0","source":{"id":"2504.13622","kind":"arxiv","version":1}},"canonical_sha256":"29b00cc909b8cee7adb2e2139d3c00e760aff754039a6296bac56a350481bd55","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"29b00cc909b8cee7adb2e2139d3c00e760aff754039a6296bac56a350481bd55","first_computed_at":"2026-07-05T10:50:59.245573Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:50:59.245573Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NVHDrKG/R/FzDIN43iS3PEdgKDjlXf3DhmeN7kGEX5y91KgEyt1h/mCIaYXQKp0DbVe+9oxW5ykChGJFHMZiBg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:50:59.246067Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.13622","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e4309a9fab173a091539a7dc12ad1b803d1027bfacf3e5054a20eabeb326d3fb","sha256:2a600e3e5cbdc1bb87d6853d3c1f55fddbb8423a70f8832e191f8cc0b85625bd"],"state_sha256":"45b6b179a07dba9af8b5dd571dea0eb3d13eedff333b4b42ce0d1be65dc6df37"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J1dZm6UYScElanRcfZL2sIcF0k97GN7AkHCDw4kFiuD2btr9nr1mgN0PdOcFvqPWooOJrvx7NcbKPn/VbAvKBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T15:43:18.036187Z","bundle_sha256":"c20985fade08580f2b395bf346e6248dd0c650ae1231d3153ce3d3901f22a596"}}