{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:FYE57ADBDWK7MFT2UZRMJNHKWL","short_pith_number":"pith:FYE57ADB","canonical_record":{"source":{"id":"1801.06397","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-19T13:21:07Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4165d13d76c93b252891c01b32e6f1393060c8157eccc7b197cc189c350e49ca","abstract_canon_sha256":"ec3b843fc7a1ee1f2b61694ea07067d25a0cf32d607a03ca3879985321f616ad"},"schema_version":"1.0"},"canonical_sha256":"2e09df80611d95f6167aa662c4b4eab2ffef124140e362b520eb390f49931717","source":{"kind":"arxiv","id":"1801.06397","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.06397","created_at":"2026-05-18T00:20:23Z"},{"alias_kind":"arxiv_version","alias_value":"1801.06397v3","created_at":"2026-05-18T00:20:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.06397","created_at":"2026-05-18T00:20:23Z"},{"alias_kind":"pith_short_12","alias_value":"FYE57ADBDWK7","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"FYE57ADBDWK7MFT2","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"FYE57ADB","created_at":"2026-05-18T12:32:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:FYE57ADBDWK7MFT2UZRMJNHKWL","target":"record","payload":{"canonical_record":{"source":{"id":"1801.06397","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-19T13:21:07Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4165d13d76c93b252891c01b32e6f1393060c8157eccc7b197cc189c350e49ca","abstract_canon_sha256":"ec3b843fc7a1ee1f2b61694ea07067d25a0cf32d607a03ca3879985321f616ad"},"schema_version":"1.0"},"canonical_sha256":"2e09df80611d95f6167aa662c4b4eab2ffef124140e362b520eb390f49931717","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:23.939689Z","signature_b64":"ONmq3CPdv/ZdnjGOVd0Ok8WtgZ9s6NlYIPXk9m8TzO5o4MeQIZP9ID64Bisn51kZxo3I8KLWWbL7REtgYo5GDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e09df80611d95f6167aa662c4b4eab2ffef124140e362b520eb390f49931717","last_reissued_at":"2026-05-18T00:20:23.939229Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:23.939229Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1801.06397","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":"2026-05-18T00:20:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HAQVse1WmitCHY3pOfeZWiZDKQXLPQ880auEff8BrAH9EruxviyfNWTclcsTaKpYO4ge0q8W+/0LlMDyNNkwDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T06:18:28.902652Z"},"content_sha256":"2c631967c016734a525ab18c30e7768915efe7a36cbe3f430639f6365bfeb9ab","schema_version":"1.0","event_id":"sha256:2c631967c016734a525ab18c30e7768915efe7a36cbe3f430639f6365bfeb9ab"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:FYE57ADBDWK7MFT2UZRMJNHKWL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.CV","authors_text":"Alexey Dosovitskiy, Caner Hazirbas, Daniel Cremers, Eddy Ilg, Nikolaus Mayer, Philipp Fischer, Thomas Brox","submitted_at":"2018-01-19T13:21:07Z","abstract_excerpt":"The finding that very large networks can be trained efficiently and reliably has led to a paradigm shift in computer vision from engineered solutions to learning formulations. As a result, the research challenge shifts from devising algorithms to creating suitable and abundant training data for supervised learning. How to efficiently create such training data? The dominant data acquisition method in visual recognition is based on web data and manual annotation. Yet, for many computer vision problems, such as stereo or optical flow estimation, this approach is not feasible because humans cannot"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.06397","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":"2026-05-18T00:20:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7EgdcgFjqXhYY7v5K9awu7XE//Aj8tSTr6O5oFc8rXRnUxa9VbjjgcFOgl1TNOJ+N5wDVZnDqOCM63mK0pYVAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T06:18:28.903332Z"},"content_sha256":"e91076a413ca075a15fd33581386ca2662558a3c59304ff3cc9522c329580556","schema_version":"1.0","event_id":"sha256:e91076a413ca075a15fd33581386ca2662558a3c59304ff3cc9522c329580556"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FYE57ADBDWK7MFT2UZRMJNHKWL/bundle.json","state_url":"https://pith.science/pith/FYE57ADBDWK7MFT2UZRMJNHKWL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FYE57ADBDWK7MFT2UZRMJNHKWL/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":"2026-05-26T06:18:28Z","links":{"resolver":"https://pith.science/pith/FYE57ADBDWK7MFT2UZRMJNHKWL","bundle":"https://pith.science/pith/FYE57ADBDWK7MFT2UZRMJNHKWL/bundle.json","state":"https://pith.science/pith/FYE57ADBDWK7MFT2UZRMJNHKWL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FYE57ADBDWK7MFT2UZRMJNHKWL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:FYE57ADBDWK7MFT2UZRMJNHKWL","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":"ec3b843fc7a1ee1f2b61694ea07067d25a0cf32d607a03ca3879985321f616ad","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-19T13:21:07Z","title_canon_sha256":"4165d13d76c93b252891c01b32e6f1393060c8157eccc7b197cc189c350e49ca"},"schema_version":"1.0","source":{"id":"1801.06397","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.06397","created_at":"2026-05-18T00:20:23Z"},{"alias_kind":"arxiv_version","alias_value":"1801.06397v3","created_at":"2026-05-18T00:20:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.06397","created_at":"2026-05-18T00:20:23Z"},{"alias_kind":"pith_short_12","alias_value":"FYE57ADBDWK7","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"FYE57ADBDWK7MFT2","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"FYE57ADB","created_at":"2026-05-18T12:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:e91076a413ca075a15fd33581386ca2662558a3c59304ff3cc9522c329580556","target":"graph","created_at":"2026-05-18T00:20:23Z","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":"The finding that very large networks can be trained efficiently and reliably has led to a paradigm shift in computer vision from engineered solutions to learning formulations. As a result, the research challenge shifts from devising algorithms to creating suitable and abundant training data for supervised learning. How to efficiently create such training data? The dominant data acquisition method in visual recognition is based on web data and manual annotation. Yet, for many computer vision problems, such as stereo or optical flow estimation, this approach is not feasible because humans cannot","authors_text":"Alexey Dosovitskiy, Caner Hazirbas, Daniel Cremers, Eddy Ilg, Nikolaus Mayer, Philipp Fischer, Thomas Brox","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-19T13:21:07Z","title":"What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.06397","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":"sha256:2c631967c016734a525ab18c30e7768915efe7a36cbe3f430639f6365bfeb9ab","target":"record","created_at":"2026-05-18T00:20:23Z","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":"ec3b843fc7a1ee1f2b61694ea07067d25a0cf32d607a03ca3879985321f616ad","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-19T13:21:07Z","title_canon_sha256":"4165d13d76c93b252891c01b32e6f1393060c8157eccc7b197cc189c350e49ca"},"schema_version":"1.0","source":{"id":"1801.06397","kind":"arxiv","version":3}},"canonical_sha256":"2e09df80611d95f6167aa662c4b4eab2ffef124140e362b520eb390f49931717","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2e09df80611d95f6167aa662c4b4eab2ffef124140e362b520eb390f49931717","first_computed_at":"2026-05-18T00:20:23.939229Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:20:23.939229Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ONmq3CPdv/ZdnjGOVd0Ok8WtgZ9s6NlYIPXk9m8TzO5o4MeQIZP9ID64Bisn51kZxo3I8KLWWbL7REtgYo5GDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:20:23.939689Z","signed_message":"canonical_sha256_bytes"},"source_id":"1801.06397","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2c631967c016734a525ab18c30e7768915efe7a36cbe3f430639f6365bfeb9ab","sha256:e91076a413ca075a15fd33581386ca2662558a3c59304ff3cc9522c329580556"],"state_sha256":"65603cae2000bb00e1e612eee3cc80ab2e8e1e313880af3c93d47cb41516e221"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vTD/9s0uU1GJzVVFzugOBTMlYqP3QoqJftY70CbBWP9Lu0J9//4vrJeDpGeOc8L3svO5JZtv0Jz2NYE10QQ+Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T06:18:28.906912Z","bundle_sha256":"b48ba99db881d11c0de112200791fd0782efc11945bc259cd482a09560b70bf1"}}