{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:5YAYT7YJ3FTME56CMRDZDEHZSU","short_pith_number":"pith:5YAYT7YJ","canonical_record":{"source":{"id":"1807.01990","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-04T05:45:48Z","cross_cats_sorted":[],"title_canon_sha256":"a0596bd3f72511daaa3ea39378e4e5c18e14cb98fb8023096ba7cd63140aaf2d","abstract_canon_sha256":"8bfe181b5531308c3f8c7100649635257dc7fae21970515c439805041b373eac"},"schema_version":"1.0"},"canonical_sha256":"ee0189ff09d966c277c264479190f99514e3067d32c87c658a154bec97f8a6b7","source":{"kind":"arxiv","id":"1807.01990","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.01990","created_at":"2026-05-18T00:11:24Z"},{"alias_kind":"arxiv_version","alias_value":"1807.01990v1","created_at":"2026-05-18T00:11:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.01990","created_at":"2026-05-18T00:11:24Z"},{"alias_kind":"pith_short_12","alias_value":"5YAYT7YJ3FTM","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5YAYT7YJ3FTME56C","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5YAYT7YJ","created_at":"2026-05-18T12:32:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:5YAYT7YJ3FTME56CMRDZDEHZSU","target":"record","payload":{"canonical_record":{"source":{"id":"1807.01990","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-04T05:45:48Z","cross_cats_sorted":[],"title_canon_sha256":"a0596bd3f72511daaa3ea39378e4e5c18e14cb98fb8023096ba7cd63140aaf2d","abstract_canon_sha256":"8bfe181b5531308c3f8c7100649635257dc7fae21970515c439805041b373eac"},"schema_version":"1.0"},"canonical_sha256":"ee0189ff09d966c277c264479190f99514e3067d32c87c658a154bec97f8a6b7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:24.543727Z","signature_b64":"cMtXN2PVcybYdn9mVD0yeYT5+xW6NxGJ0aZcPchL8Pt+dearMqph+Ur29lDp2gZJuQz0l9ARCBP9qWyKRxqaCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee0189ff09d966c277c264479190f99514e3067d32c87c658a154bec97f8a6b7","last_reissued_at":"2026-05-18T00:11:24.543150Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:24.543150Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.01990","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:11:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2lgOrNC0hl6418yXZwrV8iVWi1d0ohqG1/Pa1cn0gPr6ux1CI4istrmnvCGxdYXHRen9ri5zvvwzv2m2wGrfAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T21:12:03.014045Z"},"content_sha256":"f4dc47da95dbb7ddcd6ac0c6553a74378e291a4f870b88512eec49abe8c6e5f5","schema_version":"1.0","event_id":"sha256:f4dc47da95dbb7ddcd6ac0c6553a74378e291a4f870b88512eec49abe8c6e5f5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:5YAYT7YJ3FTME56CMRDZDEHZSU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Transfer Learning From Synthetic To Real Images Using Variational Autoencoders For Precise Position Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Giovanni De Magistris, Sakyasingha Dasgupta, Subhajit Chaudhury, Tadanobu Inoue","submitted_at":"2018-07-04T05:45:48Z","abstract_excerpt":"Capturing and labeling camera images in the real world is an expensive task, whereas synthesizing labeled images in a simulation environment is easy for collecting large-scale image data. However, learning from only synthetic images may not achieve the desired performance in the real world due to a gap between synthetic and real images. We propose a method that transfers learned detection of an object position from a simulation environment to the real world. This method uses only a significantly limited dataset of real images while leveraging a large dataset of synthetic images using variation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.01990","kind":"arxiv","version":1},"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":"2026-05-18T00:11:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2YzofGyPyCq3jX5ujzaSML4cmXcYhOkLYKPGO7fte7MW3y6R03feVc2GzE5c8dHCngccerGYYG6Uh34bhx+TDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T21:12:03.014410Z"},"content_sha256":"cf5ba8bc365eeb46b6f83a043b4fcf5af797fb13380fa17768df06249f341c74","schema_version":"1.0","event_id":"sha256:cf5ba8bc365eeb46b6f83a043b4fcf5af797fb13380fa17768df06249f341c74"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5YAYT7YJ3FTME56CMRDZDEHZSU/bundle.json","state_url":"https://pith.science/pith/5YAYT7YJ3FTME56CMRDZDEHZSU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5YAYT7YJ3FTME56CMRDZDEHZSU/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":"2026-06-22T21:12:03Z","links":{"resolver":"https://pith.science/pith/5YAYT7YJ3FTME56CMRDZDEHZSU","bundle":"https://pith.science/pith/5YAYT7YJ3FTME56CMRDZDEHZSU/bundle.json","state":"https://pith.science/pith/5YAYT7YJ3FTME56CMRDZDEHZSU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5YAYT7YJ3FTME56CMRDZDEHZSU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:5YAYT7YJ3FTME56CMRDZDEHZSU","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":"8bfe181b5531308c3f8c7100649635257dc7fae21970515c439805041b373eac","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-04T05:45:48Z","title_canon_sha256":"a0596bd3f72511daaa3ea39378e4e5c18e14cb98fb8023096ba7cd63140aaf2d"},"schema_version":"1.0","source":{"id":"1807.01990","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.01990","created_at":"2026-05-18T00:11:24Z"},{"alias_kind":"arxiv_version","alias_value":"1807.01990v1","created_at":"2026-05-18T00:11:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.01990","created_at":"2026-05-18T00:11:24Z"},{"alias_kind":"pith_short_12","alias_value":"5YAYT7YJ3FTM","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5YAYT7YJ3FTME56C","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5YAYT7YJ","created_at":"2026-05-18T12:32:08Z"}],"graph_snapshots":[{"event_id":"sha256:cf5ba8bc365eeb46b6f83a043b4fcf5af797fb13380fa17768df06249f341c74","target":"graph","created_at":"2026-05-18T00:11:24Z","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":"Capturing and labeling camera images in the real world is an expensive task, whereas synthesizing labeled images in a simulation environment is easy for collecting large-scale image data. However, learning from only synthetic images may not achieve the desired performance in the real world due to a gap between synthetic and real images. We propose a method that transfers learned detection of an object position from a simulation environment to the real world. This method uses only a significantly limited dataset of real images while leveraging a large dataset of synthetic images using variation","authors_text":"Giovanni De Magistris, Sakyasingha Dasgupta, Subhajit Chaudhury, Tadanobu Inoue","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-04T05:45:48Z","title":"Transfer Learning From Synthetic To Real Images Using Variational Autoencoders For Precise Position Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.01990","kind":"arxiv","version":1},"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:f4dc47da95dbb7ddcd6ac0c6553a74378e291a4f870b88512eec49abe8c6e5f5","target":"record","created_at":"2026-05-18T00:11:24Z","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":"8bfe181b5531308c3f8c7100649635257dc7fae21970515c439805041b373eac","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-04T05:45:48Z","title_canon_sha256":"a0596bd3f72511daaa3ea39378e4e5c18e14cb98fb8023096ba7cd63140aaf2d"},"schema_version":"1.0","source":{"id":"1807.01990","kind":"arxiv","version":1}},"canonical_sha256":"ee0189ff09d966c277c264479190f99514e3067d32c87c658a154bec97f8a6b7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ee0189ff09d966c277c264479190f99514e3067d32c87c658a154bec97f8a6b7","first_computed_at":"2026-05-18T00:11:24.543150Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:11:24.543150Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cMtXN2PVcybYdn9mVD0yeYT5+xW6NxGJ0aZcPchL8Pt+dearMqph+Ur29lDp2gZJuQz0l9ARCBP9qWyKRxqaCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:11:24.543727Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.01990","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f4dc47da95dbb7ddcd6ac0c6553a74378e291a4f870b88512eec49abe8c6e5f5","sha256:cf5ba8bc365eeb46b6f83a043b4fcf5af797fb13380fa17768df06249f341c74"],"state_sha256":"0100945aea1f3c3db824087540b803c3517c4b49e7eab654ef37a1379058b549"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lJD0QaWOg42goH6Xh2J6dYU0PDps/9XAUVXN+vdgEYpEBBMuCbWDRzpGa17QjveT4qJhaLjnOKdRbiRZaFw4BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-22T21:12:03.016348Z","bundle_sha256":"990385db19fb817e483ab736fe7597f4dbdc05b153a8cfb6b679423d1e50de20"}}