{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:VMV4763RFJ7M4OYFMGPPXZNYLG","short_pith_number":"pith:VMV4763R","canonical_record":{"source":{"id":"1808.07528","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-22T19:11:41Z","cross_cats_sorted":[],"title_canon_sha256":"3f4e329383b1e9731051e1776e4c47110c39e99fd2ea3175870e3fb3d1cea7cd","abstract_canon_sha256":"b80812eafd4d03f8c22b479821302c1ad2e268019784d0b6732348b0ffc0cd88"},"schema_version":"1.0"},"canonical_sha256":"ab2bcffb712a7ece3b05619efbe5b859bfdfdb639109628a4db55e38745a56b6","source":{"kind":"arxiv","id":"1808.07528","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.07528","created_at":"2026-05-17T23:43:17Z"},{"alias_kind":"arxiv_version","alias_value":"1808.07528v3","created_at":"2026-05-17T23:43:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.07528","created_at":"2026-05-17T23:43:17Z"},{"alias_kind":"pith_short_12","alias_value":"VMV4763RFJ7M","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"VMV4763RFJ7M4OYF","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"VMV4763R","created_at":"2026-05-18T12:32:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:VMV4763RFJ7M4OYFMGPPXZNYLG","target":"record","payload":{"canonical_record":{"source":{"id":"1808.07528","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-22T19:11:41Z","cross_cats_sorted":[],"title_canon_sha256":"3f4e329383b1e9731051e1776e4c47110c39e99fd2ea3175870e3fb3d1cea7cd","abstract_canon_sha256":"b80812eafd4d03f8c22b479821302c1ad2e268019784d0b6732348b0ffc0cd88"},"schema_version":"1.0"},"canonical_sha256":"ab2bcffb712a7ece3b05619efbe5b859bfdfdb639109628a4db55e38745a56b6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:17.644388Z","signature_b64":"xCbx3YnSH61aFjrqwOkBpbwrmoZlG+HCGhNbp0TVPEnasZWgWKuvmrKbH8HMnEQVw2NRWBYUhsktTQWkiX9KCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab2bcffb712a7ece3b05619efbe5b859bfdfdb639109628a4db55e38745a56b6","last_reissued_at":"2026-05-17T23:43:17.643715Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:17.643715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.07528","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":"2026-05-17T23:43:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ec+cbCcX6v2oQIKovgkAXvr9xHaGhapPJ0rzYXmnBMZQBcQLd8EffPN8JI6KjcaTqpaCO+LpskOl9Jc0la+yAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T19:18:29.808416Z"},"content_sha256":"7c898e8ebec94b101b522e571bbb928d89d95a7ef48ee7bdfa87c64727ed09b7","schema_version":"1.0","event_id":"sha256:7c898e8ebec94b101b522e571bbb928d89d95a7ef48ee7bdfa87c64727ed09b7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:VMV4763RFJ7M4OYFMGPPXZNYLG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Rethinking Monocular Depth Estimation with Adversarial Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alan Yuille, Faisal Mahmood, Nicholas J. Durr, Richard Chen","submitted_at":"2018-08-22T19:11:41Z","abstract_excerpt":"Monocular depth estimation is an extensively studied computer vision problem with a vast variety of applications. Deep learning-based methods have demonstrated promise for both supervised and unsupervised depth estimation from monocular images. Most existing approaches treat depth estimation as a regression problem with a local pixel-wise loss function. In this work, we innovate beyond existing approaches by using adversarial training to learn a context-aware, non-local loss function. Such an approach penalizes the joint configuration of predicted depth values at the patch-level instead of the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.07528","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":"2026-05-17T23:43:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EAxhFl2y4lk1Dlj8touWelkrc9PzQ9A+lS/I1xrMsOrtGpHphQvpb9Cpa+EWk8gpyz60ILlu9QCUApNiiWQtDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T19:18:29.809065Z"},"content_sha256":"e8cd14458119ce5e3c7b876cd59bc142cae789389b8b5d9dbfcd2ea07d50889d","schema_version":"1.0","event_id":"sha256:e8cd14458119ce5e3c7b876cd59bc142cae789389b8b5d9dbfcd2ea07d50889d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VMV4763RFJ7M4OYFMGPPXZNYLG/bundle.json","state_url":"https://pith.science/pith/VMV4763RFJ7M4OYFMGPPXZNYLG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VMV4763RFJ7M4OYFMGPPXZNYLG/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":"2026-05-25T19:18:29Z","links":{"resolver":"https://pith.science/pith/VMV4763RFJ7M4OYFMGPPXZNYLG","bundle":"https://pith.science/pith/VMV4763RFJ7M4OYFMGPPXZNYLG/bundle.json","state":"https://pith.science/pith/VMV4763RFJ7M4OYFMGPPXZNYLG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VMV4763RFJ7M4OYFMGPPXZNYLG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:VMV4763RFJ7M4OYFMGPPXZNYLG","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":"b80812eafd4d03f8c22b479821302c1ad2e268019784d0b6732348b0ffc0cd88","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-22T19:11:41Z","title_canon_sha256":"3f4e329383b1e9731051e1776e4c47110c39e99fd2ea3175870e3fb3d1cea7cd"},"schema_version":"1.0","source":{"id":"1808.07528","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.07528","created_at":"2026-05-17T23:43:17Z"},{"alias_kind":"arxiv_version","alias_value":"1808.07528v3","created_at":"2026-05-17T23:43:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.07528","created_at":"2026-05-17T23:43:17Z"},{"alias_kind":"pith_short_12","alias_value":"VMV4763RFJ7M","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"VMV4763RFJ7M4OYF","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"VMV4763R","created_at":"2026-05-18T12:32:59Z"}],"graph_snapshots":[{"event_id":"sha256:e8cd14458119ce5e3c7b876cd59bc142cae789389b8b5d9dbfcd2ea07d50889d","target":"graph","created_at":"2026-05-17T23:43:17Z","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":"Monocular depth estimation is an extensively studied computer vision problem with a vast variety of applications. Deep learning-based methods have demonstrated promise for both supervised and unsupervised depth estimation from monocular images. Most existing approaches treat depth estimation as a regression problem with a local pixel-wise loss function. In this work, we innovate beyond existing approaches by using adversarial training to learn a context-aware, non-local loss function. Such an approach penalizes the joint configuration of predicted depth values at the patch-level instead of the","authors_text":"Alan Yuille, Faisal Mahmood, Nicholas J. Durr, Richard Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-22T19:11:41Z","title":"Rethinking Monocular Depth Estimation with Adversarial Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.07528","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":"sha256:7c898e8ebec94b101b522e571bbb928d89d95a7ef48ee7bdfa87c64727ed09b7","target":"record","created_at":"2026-05-17T23:43:17Z","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":"b80812eafd4d03f8c22b479821302c1ad2e268019784d0b6732348b0ffc0cd88","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-22T19:11:41Z","title_canon_sha256":"3f4e329383b1e9731051e1776e4c47110c39e99fd2ea3175870e3fb3d1cea7cd"},"schema_version":"1.0","source":{"id":"1808.07528","kind":"arxiv","version":3}},"canonical_sha256":"ab2bcffb712a7ece3b05619efbe5b859bfdfdb639109628a4db55e38745a56b6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ab2bcffb712a7ece3b05619efbe5b859bfdfdb639109628a4db55e38745a56b6","first_computed_at":"2026-05-17T23:43:17.643715Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:17.643715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xCbx3YnSH61aFjrqwOkBpbwrmoZlG+HCGhNbp0TVPEnasZWgWKuvmrKbH8HMnEQVw2NRWBYUhsktTQWkiX9KCQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:17.644388Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.07528","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7c898e8ebec94b101b522e571bbb928d89d95a7ef48ee7bdfa87c64727ed09b7","sha256:e8cd14458119ce5e3c7b876cd59bc142cae789389b8b5d9dbfcd2ea07d50889d"],"state_sha256":"417d7c504d7f810964948b1fbc12f27b1921cc99068ee17a1c966da3c3a4de18"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O5SdOVgCl4r+TDNlegT7DWDOM5k3aP+dWNiBkOsImhvryI+UGFNkURNuK2gKkwQHunlaLweg1WtaacPSLBkYDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T19:18:29.812667Z","bundle_sha256":"c36ecd098d29bad33b3d7c14daa42773210eb2121880e1f3c983ac28effadb87"}}