{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:3L2JDX3OOEDGTJKK3NIQCFPACO","short_pith_number":"pith:3L2JDX3O","schema_version":"1.0","canonical_sha256":"daf491df6e710669a54adb510115e0139f7a4469fb6834ba26175d0f9c11fe13","source":{"kind":"arxiv","id":"1609.04802","version":5},"attestation_state":"computed","paper":{"title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.CV","authors_text":"Alejandro Acosta, Alykhan Tejani, Andrew Aitken, Andrew Cunningham, Christian Ledig, Ferenc Huszar, Johannes Totz, Jose Caballero, Lucas Theis, Wenzhe Shi, Zehan Wang","submitted_at":"2016-09-15T19:53:07Z","abstract_excerpt":"Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and"},"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":"1609.04802","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-15T19:53:07Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"b543607a6a3d252cabd2d0d8dbbf1f2f669d9a37611c7f395e39cc84f4fd2cbe","abstract_canon_sha256":"c5fc6e5ba1cd3d603967362e22e3454e529233ff0372c5f99354a3a4bbee83d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:43:43.467740Z","signature_b64":"2+SlzVdF9QsvG2iN+ynfnh7Q9fuBkLUk2LiDUdjHHiZ1v9MDaO6KWtBkgW4xLy0qndm6RpRNuCYLqTRDer0EAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"daf491df6e710669a54adb510115e0139f7a4469fb6834ba26175d0f9c11fe13","last_reissued_at":"2026-05-18T00:43:43.467118Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:43:43.467118Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.CV","authors_text":"Alejandro Acosta, Alykhan Tejani, Andrew Aitken, Andrew Cunningham, Christian Ledig, Ferenc Huszar, Johannes Totz, Jose Caballero, Lucas Theis, Wenzhe Shi, Zehan Wang","submitted_at":"2016-09-15T19:53:07Z","abstract_excerpt":"Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.04802","kind":"arxiv","version":5},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1609.04802","created_at":"2026-05-18T00:43:43.467217+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.04802v5","created_at":"2026-05-18T00:43:43.467217+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.04802","created_at":"2026-05-18T00:43:43.467217+00:00"},{"alias_kind":"pith_short_12","alias_value":"3L2JDX3OOEDG","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_16","alias_value":"3L2JDX3OOEDGTJKK","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_8","alias_value":"3L2JDX3O","created_at":"2026-05-18T12:29:55.572404+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":5,"sample":[{"citing_arxiv_id":"1906.11080","citing_title":"AGAN: Towards Automated Design of Generative Adversarial Networks","ref_index":2,"is_internal_anchor":true},{"citing_arxiv_id":"1906.11467","citing_title":"Abnormal Colon Polyp Image Synthesis Using Conditional Adversarial Networks for Improved Detection Performance","ref_index":18,"is_internal_anchor":true},{"citing_arxiv_id":"1907.10178","citing_title":"Analyzing the Variety Loss in the Context of Probabilistic Trajectory Prediction","ref_index":25,"is_internal_anchor":true},{"citing_arxiv_id":"1907.11341","citing_title":"Image Enhancement by Recurrently-trained Super-resolution Network","ref_index":10,"is_internal_anchor":true},{"citing_arxiv_id":"2605.16476","citing_title":"Deep Learning for MRI Slice Interpolation: The Critical Role of Problem Formulation","ref_index":7,"is_internal_anchor":true},{"citing_arxiv_id":"1710.10196","citing_title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20684","citing_title":"CKM Beyond Channel Gain: Spatial Correlation Map Construction with Deep Learning","ref_index":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3L2JDX3OOEDGTJKK3NIQCFPACO","json":"https://pith.science/pith/3L2JDX3OOEDGTJKK3NIQCFPACO.json","graph_json":"https://pith.science/api/pith-number/3L2JDX3OOEDGTJKK3NIQCFPACO/graph.json","events_json":"https://pith.science/api/pith-number/3L2JDX3OOEDGTJKK3NIQCFPACO/events.json","paper":"https://pith.science/paper/3L2JDX3O"},"agent_actions":{"view_html":"https://pith.science/pith/3L2JDX3OOEDGTJKK3NIQCFPACO","download_json":"https://pith.science/pith/3L2JDX3OOEDGTJKK3NIQCFPACO.json","view_paper":"https://pith.science/paper/3L2JDX3O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.04802&json=true","fetch_graph":"https://pith.science/api/pith-number/3L2JDX3OOEDGTJKK3NIQCFPACO/graph.json","fetch_events":"https://pith.science/api/pith-number/3L2JDX3OOEDGTJKK3NIQCFPACO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3L2JDX3OOEDGTJKK3NIQCFPACO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3L2JDX3OOEDGTJKK3NIQCFPACO/action/storage_attestation","attest_author":"https://pith.science/pith/3L2JDX3OOEDGTJKK3NIQCFPACO/action/author_attestation","sign_citation":"https://pith.science/pith/3L2JDX3OOEDGTJKK3NIQCFPACO/action/citation_signature","submit_replication":"https://pith.science/pith/3L2JDX3OOEDGTJKK3NIQCFPACO/action/replication_record"}},"created_at":"2026-05-18T00:43:43.467217+00:00","updated_at":"2026-05-18T00:43:43.467217+00:00"}