{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:D63I77NN27FD5FTDNWL5S7FAKH","short_pith_number":"pith:D63I77NN","schema_version":"1.0","canonical_sha256":"1fb68ffdadd7ca3e96636d97d97ca051c86d6b266c7e59fd14ed71b4460c4011","source":{"kind":"arxiv","id":"1808.04432","version":1},"attestation_state":"computed","paper":{"title":"X-GANs: Image Reconstruction Made Easy for Extreme Cases","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.CV","authors_text":"Guoping Wang, Longfei Liu, Sheng Li, Yisong Chen","submitted_at":"2018-08-06T10:36:53Z","abstract_excerpt":"Image reconstruction including image restoration and denoising is a challenging problem in the field of image computing. We present a new method, called X-GANs, for reconstruction of arbitrary corrupted resource based on a variant of conditional generative adversarial networks (conditional GANs). In our method, a novel generator and multi-scale discriminators are proposed, as well as the combined adversarial losses, which integrate a VGG perceptual loss, an adversarial perceptual loss, and an elaborate corresponding point loss together based on the analysis of image feature. Our conditional GA"},"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":"1808.04432","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-06T10:36:53Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"e4a8fd8e399345dcc3e8d03defbd5d03e25beebb0e233952c83cb12365f9e9b1","abstract_canon_sha256":"08946def42464610422f66a2714522611971888c67a5d598232dcf0a915e42d5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:12.439212Z","signature_b64":"KwguJaR1gzWzcWqbCwjmupjzVJc7oFhRuimWU46lArtntaVtrfy1hj2vt0hA25YXG8NiPEgS/UHyLBVmDBfpBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1fb68ffdadd7ca3e96636d97d97ca051c86d6b266c7e59fd14ed71b4460c4011","last_reissued_at":"2026-05-18T00:08:12.438831Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:12.438831Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"X-GANs: Image Reconstruction Made Easy for Extreme Cases","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.CV","authors_text":"Guoping Wang, Longfei Liu, Sheng Li, Yisong Chen","submitted_at":"2018-08-06T10:36:53Z","abstract_excerpt":"Image reconstruction including image restoration and denoising is a challenging problem in the field of image computing. We present a new method, called X-GANs, for reconstruction of arbitrary corrupted resource based on a variant of conditional generative adversarial networks (conditional GANs). In our method, a novel generator and multi-scale discriminators are proposed, as well as the combined adversarial losses, which integrate a VGG perceptual loss, an adversarial perceptual loss, and an elaborate corresponding point loss together based on the analysis of image feature. Our conditional GA"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.04432","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":""},"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":"1808.04432","created_at":"2026-05-18T00:08:12.438893+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.04432v1","created_at":"2026-05-18T00:08:12.438893+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.04432","created_at":"2026-05-18T00:08:12.438893+00:00"},{"alias_kind":"pith_short_12","alias_value":"D63I77NN27FD","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"D63I77NN27FD5FTD","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"D63I77NN","created_at":"2026-05-18T12:32:19.392346+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/D63I77NN27FD5FTDNWL5S7FAKH","json":"https://pith.science/pith/D63I77NN27FD5FTDNWL5S7FAKH.json","graph_json":"https://pith.science/api/pith-number/D63I77NN27FD5FTDNWL5S7FAKH/graph.json","events_json":"https://pith.science/api/pith-number/D63I77NN27FD5FTDNWL5S7FAKH/events.json","paper":"https://pith.science/paper/D63I77NN"},"agent_actions":{"view_html":"https://pith.science/pith/D63I77NN27FD5FTDNWL5S7FAKH","download_json":"https://pith.science/pith/D63I77NN27FD5FTDNWL5S7FAKH.json","view_paper":"https://pith.science/paper/D63I77NN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.04432&json=true","fetch_graph":"https://pith.science/api/pith-number/D63I77NN27FD5FTDNWL5S7FAKH/graph.json","fetch_events":"https://pith.science/api/pith-number/D63I77NN27FD5FTDNWL5S7FAKH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D63I77NN27FD5FTDNWL5S7FAKH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D63I77NN27FD5FTDNWL5S7FAKH/action/storage_attestation","attest_author":"https://pith.science/pith/D63I77NN27FD5FTDNWL5S7FAKH/action/author_attestation","sign_citation":"https://pith.science/pith/D63I77NN27FD5FTDNWL5S7FAKH/action/citation_signature","submit_replication":"https://pith.science/pith/D63I77NN27FD5FTDNWL5S7FAKH/action/replication_record"}},"created_at":"2026-05-18T00:08:12.438893+00:00","updated_at":"2026-05-18T00:08:12.438893+00:00"}