{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:OJ4YOZSQMG6DPBRE435ZFHKIOR","short_pith_number":"pith:OJ4YOZSQ","canonical_record":{"source":{"id":"1611.09969","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-30T01:58:54Z","cross_cats_sorted":[],"title_canon_sha256":"df3e688d4e740137dff26ccd3343d1b7e1ead58edb1f18d44526264d886852a0","abstract_canon_sha256":"09620b564e8b445d8401bb05b412051904c53b6d4399876ee07c9816cbba2af1"},"schema_version":"1.0"},"canonical_sha256":"727987665061bc378624e6fb929d4874621284cfe1c198958b176099c37c43aa","source":{"kind":"arxiv","id":"1611.09969","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.09969","created_at":"2026-05-18T00:46:26Z"},{"alias_kind":"arxiv_version","alias_value":"1611.09969v2","created_at":"2026-05-18T00:46:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.09969","created_at":"2026-05-18T00:46:26Z"},{"alias_kind":"pith_short_12","alias_value":"OJ4YOZSQMG6D","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_16","alias_value":"OJ4YOZSQMG6DPBRE","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_8","alias_value":"OJ4YOZSQ","created_at":"2026-05-18T12:30:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:OJ4YOZSQMG6DPBRE435ZFHKIOR","target":"record","payload":{"canonical_record":{"source":{"id":"1611.09969","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-30T01:58:54Z","cross_cats_sorted":[],"title_canon_sha256":"df3e688d4e740137dff26ccd3343d1b7e1ead58edb1f18d44526264d886852a0","abstract_canon_sha256":"09620b564e8b445d8401bb05b412051904c53b6d4399876ee07c9816cbba2af1"},"schema_version":"1.0"},"canonical_sha256":"727987665061bc378624e6fb929d4874621284cfe1c198958b176099c37c43aa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:26.209638Z","signature_b64":"cLkoBL1diUtPvZSqkdM45iyoFHymOQ/IjTF4/FA66JklIgfe3oST1+rQFK5iaq1InWbWgJBQ55F9hYb+gZrXCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"727987665061bc378624e6fb929d4874621284cfe1c198958b176099c37c43aa","last_reissued_at":"2026-05-18T00:46:26.209113Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:26.209113Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.09969","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:46:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B+30OGr6j54j7nJp3Y0jtXRlyy3Lf/Ox3RISUxR5WD8/dnm7URRKsq5+v3muqg0HxoyAGC3HnQtB12Kdp3ujDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T21:58:55.743538Z"},"content_sha256":"7be13feb09a6d2be096abbbff0463b851c5ebc73d68b8c473318ee05750e051c","schema_version":"1.0","event_id":"sha256:7be13feb09a6d2be096abbbff0463b851c5ebc73d68b8c473318ee05750e051c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:OJ4YOZSQMG6DPBRE435ZFHKIOR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chao Yang, Eli Shechtman, Hao Li, Oliver Wang, Xin Lu, Zhe Lin","submitted_at":"2016-11-30T01:58:54Z","abstract_excerpt":"Recent advances in deep learning have shown exciting promise in filling large holes in natural images with semantically plausible and context aware details, impacting fundamental image manipulation tasks such as object removal. While these learning-based methods are significantly more effective in capturing high-level features than prior techniques, they can only handle very low-resolution inputs due to memory limitations and difficulty in training. Even for slightly larger images, the inpainted regions would appear blurry and unpleasant boundaries become visible. We propose a multi-scale neur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.09969","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":""},"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":"2026-05-18T00:46:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p2VGg8/wdNlaCWncsh/g3CnJPqSDigsVlzgEAyAgkGzyqMgN2r1dJGaAmarCT6i6ypv6ZDa1VxSktH/tURFVAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T21:58:55.743899Z"},"content_sha256":"90ad3861b56ae7f89e38b02b1eba1ecaea05f74981f8928e291878c0e0869ee3","schema_version":"1.0","event_id":"sha256:90ad3861b56ae7f89e38b02b1eba1ecaea05f74981f8928e291878c0e0869ee3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OJ4YOZSQMG6DPBRE435ZFHKIOR/bundle.json","state_url":"https://pith.science/pith/OJ4YOZSQMG6DPBRE435ZFHKIOR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OJ4YOZSQMG6DPBRE435ZFHKIOR/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":"2026-06-02T21:58:55Z","links":{"resolver":"https://pith.science/pith/OJ4YOZSQMG6DPBRE435ZFHKIOR","bundle":"https://pith.science/pith/OJ4YOZSQMG6DPBRE435ZFHKIOR/bundle.json","state":"https://pith.science/pith/OJ4YOZSQMG6DPBRE435ZFHKIOR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OJ4YOZSQMG6DPBRE435ZFHKIOR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:OJ4YOZSQMG6DPBRE435ZFHKIOR","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":"09620b564e8b445d8401bb05b412051904c53b6d4399876ee07c9816cbba2af1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-30T01:58:54Z","title_canon_sha256":"df3e688d4e740137dff26ccd3343d1b7e1ead58edb1f18d44526264d886852a0"},"schema_version":"1.0","source":{"id":"1611.09969","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.09969","created_at":"2026-05-18T00:46:26Z"},{"alias_kind":"arxiv_version","alias_value":"1611.09969v2","created_at":"2026-05-18T00:46:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.09969","created_at":"2026-05-18T00:46:26Z"},{"alias_kind":"pith_short_12","alias_value":"OJ4YOZSQMG6D","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_16","alias_value":"OJ4YOZSQMG6DPBRE","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_8","alias_value":"OJ4YOZSQ","created_at":"2026-05-18T12:30:36Z"}],"graph_snapshots":[{"event_id":"sha256:90ad3861b56ae7f89e38b02b1eba1ecaea05f74981f8928e291878c0e0869ee3","target":"graph","created_at":"2026-05-18T00:46:26Z","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"},"paper":{"abstract_excerpt":"Recent advances in deep learning have shown exciting promise in filling large holes in natural images with semantically plausible and context aware details, impacting fundamental image manipulation tasks such as object removal. While these learning-based methods are significantly more effective in capturing high-level features than prior techniques, they can only handle very low-resolution inputs due to memory limitations and difficulty in training. Even for slightly larger images, the inpainted regions would appear blurry and unpleasant boundaries become visible. We propose a multi-scale neur","authors_text":"Chao Yang, Eli Shechtman, Hao Li, Oliver Wang, Xin Lu, Zhe Lin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-30T01:58:54Z","title":"High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.09969","kind":"arxiv","version":2},"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":"sha256:7be13feb09a6d2be096abbbff0463b851c5ebc73d68b8c473318ee05750e051c","target":"record","created_at":"2026-05-18T00:46:26Z","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":"09620b564e8b445d8401bb05b412051904c53b6d4399876ee07c9816cbba2af1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-30T01:58:54Z","title_canon_sha256":"df3e688d4e740137dff26ccd3343d1b7e1ead58edb1f18d44526264d886852a0"},"schema_version":"1.0","source":{"id":"1611.09969","kind":"arxiv","version":2}},"canonical_sha256":"727987665061bc378624e6fb929d4874621284cfe1c198958b176099c37c43aa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"727987665061bc378624e6fb929d4874621284cfe1c198958b176099c37c43aa","first_computed_at":"2026-05-18T00:46:26.209113Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:46:26.209113Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cLkoBL1diUtPvZSqkdM45iyoFHymOQ/IjTF4/FA66JklIgfe3oST1+rQFK5iaq1InWbWgJBQ55F9hYb+gZrXCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:46:26.209638Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.09969","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7be13feb09a6d2be096abbbff0463b851c5ebc73d68b8c473318ee05750e051c","sha256:90ad3861b56ae7f89e38b02b1eba1ecaea05f74981f8928e291878c0e0869ee3"],"state_sha256":"f07d0a0e698ad69ddf8174d1c59c63cf1c317c0960a967e3332a78ff855cecba"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+3WlZln9awwd6zo11HaoIScizI6I/NVdFq5GNZerqhc/fpFA4syh+FJzceu6QCifq+Fa5+8euQIsS298Kq+3Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T21:58:55.746178Z","bundle_sha256":"8b6dc06e7c34d73385df64274454ce3ceef89cb047c2742db4da1e441d18f7a6"}}