{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:4JBBGDRBFBULLVUSXH5PJMK523","short_pith_number":"pith:4JBBGDRB","canonical_record":{"source":{"id":"1712.01056","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-04T13:16:33Z","cross_cats_sorted":[],"title_canon_sha256":"9274fd8dd99897a2f121042c67e4a0e2b747fbf985310be9df4e89334785b551","abstract_canon_sha256":"2b158ad0d460839c4028bad82b1983f6d320abb597b12cdc1832fa963b25b285"},"schema_version":"1.0"},"canonical_sha256":"e242130e212868b5d692b9faf4b15dd6f103de8d21215e029d21f8fa11a6ea24","source":{"kind":"arxiv","id":"1712.01056","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.01056","created_at":"2026-05-18T00:19:34Z"},{"alias_kind":"arxiv_version","alias_value":"1712.01056v2","created_at":"2026-05-18T00:19:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.01056","created_at":"2026-05-18T00:19:34Z"},{"alias_kind":"pith_short_12","alias_value":"4JBBGDRBFBUL","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"4JBBGDRBFBULLVUS","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"4JBBGDRB","created_at":"2026-05-18T12:31:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:4JBBGDRBFBULLVUSXH5PJMK523","target":"record","payload":{"canonical_record":{"source":{"id":"1712.01056","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-04T13:16:33Z","cross_cats_sorted":[],"title_canon_sha256":"9274fd8dd99897a2f121042c67e4a0e2b747fbf985310be9df4e89334785b551","abstract_canon_sha256":"2b158ad0d460839c4028bad82b1983f6d320abb597b12cdc1832fa963b25b285"},"schema_version":"1.0"},"canonical_sha256":"e242130e212868b5d692b9faf4b15dd6f103de8d21215e029d21f8fa11a6ea24","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:34.986255Z","signature_b64":"AErE8ryjlTVKKIB6zlFGMuQefCmr7fEg/0SiDih0E33/fA8kokWgzJ8s3W1WDIFXk2uQwC/rzXhIcyw8fx2oCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e242130e212868b5d692b9faf4b15dd6f103de8d21215e029d21f8fa11a6ea24","last_reissued_at":"2026-05-18T00:19:34.985723Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:34.985723Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1712.01056","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":"2026-05-18T00:19:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sWDO7Q2vRjZAu4+8Jc4+O6UNKZUJH44+u2Emvy40Jcxmg78J0n4drUe0UB45eZs/Qywl3o+udxWki95tA1zQAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T03:08:36.652215Z"},"content_sha256":"d770627f8dab7c92051d10059ad8f89a228ae1a10c58f8432be97c2ea776eb3b","schema_version":"1.0","event_id":"sha256:d770627f8dab7c92051d10059ad8f89a228ae1a10c58f8432be97c2ea776eb3b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:4JBBGDRBFBULLVUSXH5PJMK523","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anil S. Baslamisli, Hoang-An Le, Theo Gevers","submitted_at":"2017-12-04T13:16:33Z","abstract_excerpt":"Most of the traditional work on intrinsic image decomposition rely on deriving priors about scene characteristics. On the other hand, recent research use deep learning models as in-and-out black box and do not consider the well-established, traditional image formation process as the basis of their intrinsic learning process. As a consequence, although current deep learning approaches show superior performance when considering quantitative benchmark results, traditional approaches are still dominant in achieving high qualitative results. In this paper, the aim is to exploit the best of the two "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.01056","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":"2026-05-18T00:19:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T7b7C3kfviykc3a+QcSROZjykX6ZWiczCsa9hFvsimmNF7mw/IAOSzazuzydQA3OYZbghV8oqK1q8WeVunH5BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T03:08:36.652583Z"},"content_sha256":"18999153b6c0a4773aaa9dfe16a8a8cfb6074d98bc6d7e87095695b9b1297067","schema_version":"1.0","event_id":"sha256:18999153b6c0a4773aaa9dfe16a8a8cfb6074d98bc6d7e87095695b9b1297067"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4JBBGDRBFBULLVUSXH5PJMK523/bundle.json","state_url":"https://pith.science/pith/4JBBGDRBFBULLVUSXH5PJMK523/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4JBBGDRBFBULLVUSXH5PJMK523/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":"2026-06-08T03:08:36Z","links":{"resolver":"https://pith.science/pith/4JBBGDRBFBULLVUSXH5PJMK523","bundle":"https://pith.science/pith/4JBBGDRBFBULLVUSXH5PJMK523/bundle.json","state":"https://pith.science/pith/4JBBGDRBFBULLVUSXH5PJMK523/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4JBBGDRBFBULLVUSXH5PJMK523/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:4JBBGDRBFBULLVUSXH5PJMK523","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":"2b158ad0d460839c4028bad82b1983f6d320abb597b12cdc1832fa963b25b285","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-04T13:16:33Z","title_canon_sha256":"9274fd8dd99897a2f121042c67e4a0e2b747fbf985310be9df4e89334785b551"},"schema_version":"1.0","source":{"id":"1712.01056","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.01056","created_at":"2026-05-18T00:19:34Z"},{"alias_kind":"arxiv_version","alias_value":"1712.01056v2","created_at":"2026-05-18T00:19:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.01056","created_at":"2026-05-18T00:19:34Z"},{"alias_kind":"pith_short_12","alias_value":"4JBBGDRBFBUL","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"4JBBGDRBFBULLVUS","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"4JBBGDRB","created_at":"2026-05-18T12:31:00Z"}],"graph_snapshots":[{"event_id":"sha256:18999153b6c0a4773aaa9dfe16a8a8cfb6074d98bc6d7e87095695b9b1297067","target":"graph","created_at":"2026-05-18T00:19:34Z","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":"Most of the traditional work on intrinsic image decomposition rely on deriving priors about scene characteristics. On the other hand, recent research use deep learning models as in-and-out black box and do not consider the well-established, traditional image formation process as the basis of their intrinsic learning process. As a consequence, although current deep learning approaches show superior performance when considering quantitative benchmark results, traditional approaches are still dominant in achieving high qualitative results. In this paper, the aim is to exploit the best of the two ","authors_text":"Anil S. Baslamisli, Hoang-An Le, Theo Gevers","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-04T13:16:33Z","title":"CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.01056","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:d770627f8dab7c92051d10059ad8f89a228ae1a10c58f8432be97c2ea776eb3b","target":"record","created_at":"2026-05-18T00:19:34Z","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":"2b158ad0d460839c4028bad82b1983f6d320abb597b12cdc1832fa963b25b285","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-04T13:16:33Z","title_canon_sha256":"9274fd8dd99897a2f121042c67e4a0e2b747fbf985310be9df4e89334785b551"},"schema_version":"1.0","source":{"id":"1712.01056","kind":"arxiv","version":2}},"canonical_sha256":"e242130e212868b5d692b9faf4b15dd6f103de8d21215e029d21f8fa11a6ea24","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e242130e212868b5d692b9faf4b15dd6f103de8d21215e029d21f8fa11a6ea24","first_computed_at":"2026-05-18T00:19:34.985723Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:19:34.985723Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AErE8ryjlTVKKIB6zlFGMuQefCmr7fEg/0SiDih0E33/fA8kokWgzJ8s3W1WDIFXk2uQwC/rzXhIcyw8fx2oCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:19:34.986255Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.01056","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d770627f8dab7c92051d10059ad8f89a228ae1a10c58f8432be97c2ea776eb3b","sha256:18999153b6c0a4773aaa9dfe16a8a8cfb6074d98bc6d7e87095695b9b1297067"],"state_sha256":"12032e45a27abccb2aad2bc48493d85958011cc8558f554e13be9ddc1a916ecb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rFTTCoY99LxjExLm96IBq53+jBL2Oe8731COxoBkUtxGFS1D9D7U73E+nhIoXcG3D56SikK9SOWs0Mfrua2fAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T03:08:36.655377Z","bundle_sha256":"b6592fbe710ca49d24c1c66f51b52262a37b5f0c5e198e9c7f79200b8b614c98"}}