{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:LNJPQZZ6MXNWUJWDYKJFPAPO6X","short_pith_number":"pith:LNJPQZZ6","canonical_record":{"source":{"id":"1603.06668","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-03-22T04:08:01Z","cross_cats_sorted":[],"title_canon_sha256":"f9aa3f0579ec6a2bfc50170cde447adeb1776db74dcf3e1744eedd033731e143","abstract_canon_sha256":"e878709a46c698b52fc382ccdb8fab5e43b23b5142d22c7b0ac80e1b5632ce63"},"schema_version":"1.0"},"canonical_sha256":"5b52f8673e65db6a26c3c2925781eef5c82b230d3f162d99a6249044c9a6e770","source":{"kind":"arxiv","id":"1603.06668","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.06668","created_at":"2026-05-18T00:38:11Z"},{"alias_kind":"arxiv_version","alias_value":"1603.06668v3","created_at":"2026-05-18T00:38:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.06668","created_at":"2026-05-18T00:38:11Z"},{"alias_kind":"pith_short_12","alias_value":"LNJPQZZ6MXNW","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"LNJPQZZ6MXNWUJWD","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"LNJPQZZ6","created_at":"2026-05-18T12:30:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:LNJPQZZ6MXNWUJWDYKJFPAPO6X","target":"record","payload":{"canonical_record":{"source":{"id":"1603.06668","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-03-22T04:08:01Z","cross_cats_sorted":[],"title_canon_sha256":"f9aa3f0579ec6a2bfc50170cde447adeb1776db74dcf3e1744eedd033731e143","abstract_canon_sha256":"e878709a46c698b52fc382ccdb8fab5e43b23b5142d22c7b0ac80e1b5632ce63"},"schema_version":"1.0"},"canonical_sha256":"5b52f8673e65db6a26c3c2925781eef5c82b230d3f162d99a6249044c9a6e770","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:11.529519Z","signature_b64":"7WZKt2K4o59mawxTJqu4+yAJ6rIwEH0mKsLb+HdjzUZh6zmMnj4dLDu2aK4j+YXONxtBlyFlDNYaTVyuF2N3Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b52f8673e65db6a26c3c2925781eef5c82b230d3f162d99a6249044c9a6e770","last_reissued_at":"2026-05-18T00:38:11.528728Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:11.528728Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1603.06668","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":"2026-05-18T00:38:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q09fpsWaAwZ0cS1TZiXRpQa1QqT7M1IukNJyXBRL+PzhoZySR53/ZkDt732sIDnvX+s9SnycVUFVOS65KwviBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T21:58:40.257058Z"},"content_sha256":"167c899132c3a74cc9238a0de2255267c0e41924cba9151a57248955cab1b6fc","schema_version":"1.0","event_id":"sha256:167c899132c3a74cc9238a0de2255267c0e41924cba9151a57248955cab1b6fc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:LNJPQZZ6MXNWUJWDYKJFPAPO6X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Representations for Automatic Colorization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gregory Shakhnarovich, Gustav Larsson, Michael Maire","submitted_at":"2016-03-22T04:08:01Z","abstract_excerpt":"We develop a fully automatic image colorization system. Our approach leverages recent advances in deep networks, exploiting both low-level and semantic representations. As many scene elements naturally appear according to multimodal color distributions, we train our model to predict per-pixel color histograms. This intermediate output can be used to automatically generate a color image, or further manipulated prior to image formation. On both fully and partially automatic colorization tasks, we outperform existing methods. We also explore colorization as a vehicle for self-supervised visual re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.06668","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":""},"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":"2026-05-18T00:38:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NtAcb3ol6IyweOoRbjz+00Oz67GCaK8grFfZIndEQEhNHdJuiKJedo4RouZTuNzq/5yPr88uhg6K5uISq+AaCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T21:58:40.257408Z"},"content_sha256":"dd95b087e724dd0dac18329241e6e0824a59c7dd092afaa4dc2808bab08e64be","schema_version":"1.0","event_id":"sha256:dd95b087e724dd0dac18329241e6e0824a59c7dd092afaa4dc2808bab08e64be"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LNJPQZZ6MXNWUJWDYKJFPAPO6X/bundle.json","state_url":"https://pith.science/pith/LNJPQZZ6MXNWUJWDYKJFPAPO6X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LNJPQZZ6MXNWUJWDYKJFPAPO6X/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":"2026-06-01T21:58:40Z","links":{"resolver":"https://pith.science/pith/LNJPQZZ6MXNWUJWDYKJFPAPO6X","bundle":"https://pith.science/pith/LNJPQZZ6MXNWUJWDYKJFPAPO6X/bundle.json","state":"https://pith.science/pith/LNJPQZZ6MXNWUJWDYKJFPAPO6X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LNJPQZZ6MXNWUJWDYKJFPAPO6X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:LNJPQZZ6MXNWUJWDYKJFPAPO6X","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":"e878709a46c698b52fc382ccdb8fab5e43b23b5142d22c7b0ac80e1b5632ce63","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-03-22T04:08:01Z","title_canon_sha256":"f9aa3f0579ec6a2bfc50170cde447adeb1776db74dcf3e1744eedd033731e143"},"schema_version":"1.0","source":{"id":"1603.06668","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.06668","created_at":"2026-05-18T00:38:11Z"},{"alias_kind":"arxiv_version","alias_value":"1603.06668v3","created_at":"2026-05-18T00:38:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.06668","created_at":"2026-05-18T00:38:11Z"},{"alias_kind":"pith_short_12","alias_value":"LNJPQZZ6MXNW","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"LNJPQZZ6MXNWUJWD","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"LNJPQZZ6","created_at":"2026-05-18T12:30:29Z"}],"graph_snapshots":[{"event_id":"sha256:dd95b087e724dd0dac18329241e6e0824a59c7dd092afaa4dc2808bab08e64be","target":"graph","created_at":"2026-05-18T00:38:11Z","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":"We develop a fully automatic image colorization system. Our approach leverages recent advances in deep networks, exploiting both low-level and semantic representations. As many scene elements naturally appear according to multimodal color distributions, we train our model to predict per-pixel color histograms. This intermediate output can be used to automatically generate a color image, or further manipulated prior to image formation. On both fully and partially automatic colorization tasks, we outperform existing methods. We also explore colorization as a vehicle for self-supervised visual re","authors_text":"Gregory Shakhnarovich, Gustav Larsson, Michael Maire","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-03-22T04:08:01Z","title":"Learning Representations for Automatic Colorization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.06668","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":"sha256:167c899132c3a74cc9238a0de2255267c0e41924cba9151a57248955cab1b6fc","target":"record","created_at":"2026-05-18T00:38:11Z","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":"e878709a46c698b52fc382ccdb8fab5e43b23b5142d22c7b0ac80e1b5632ce63","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-03-22T04:08:01Z","title_canon_sha256":"f9aa3f0579ec6a2bfc50170cde447adeb1776db74dcf3e1744eedd033731e143"},"schema_version":"1.0","source":{"id":"1603.06668","kind":"arxiv","version":3}},"canonical_sha256":"5b52f8673e65db6a26c3c2925781eef5c82b230d3f162d99a6249044c9a6e770","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5b52f8673e65db6a26c3c2925781eef5c82b230d3f162d99a6249044c9a6e770","first_computed_at":"2026-05-18T00:38:11.528728Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:38:11.528728Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7WZKt2K4o59mawxTJqu4+yAJ6rIwEH0mKsLb+HdjzUZh6zmMnj4dLDu2aK4j+YXONxtBlyFlDNYaTVyuF2N3Ag==","signature_status":"signed_v1","signed_at":"2026-05-18T00:38:11.529519Z","signed_message":"canonical_sha256_bytes"},"source_id":"1603.06668","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:167c899132c3a74cc9238a0de2255267c0e41924cba9151a57248955cab1b6fc","sha256:dd95b087e724dd0dac18329241e6e0824a59c7dd092afaa4dc2808bab08e64be"],"state_sha256":"e175bd50402f9bcbac6f509ab3cff69ddb6817779bc1bf74fe77091d840ff590"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XqxYtCuS8FWNHNUnSweaQSa0/QjkgP8I07Iivy/pxTIqiNcCrRxx4CidL33gEGL7xHslUxAbmXsw3QunrcFWBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T21:58:40.259368Z","bundle_sha256":"6aad08fa8d5fcee87864a2e92b15c93faea7761aa0204e57496dc9d09d6e3029"}}