{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:RJGHE5CSNYQ72KQJF2ZXBPPCVI","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":"95d39cf118b5e7f0b2719ab21816f214186dc9d6decf2a7c49eb217e2268c135","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-04T17:38:05Z","title_canon_sha256":"1581337e199ede26dd644ad37142aead787b9ac522892a6e7cc7e642539e9b83"},"schema_version":"1.0","source":{"id":"1909.01960","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.01960","created_at":"2026-07-05T00:02:22Z"},{"alias_kind":"arxiv_version","alias_value":"1909.01960v1","created_at":"2026-07-05T00:02:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.01960","created_at":"2026-07-05T00:02:22Z"},{"alias_kind":"pith_short_12","alias_value":"RJGHE5CSNYQ7","created_at":"2026-07-05T00:02:22Z"},{"alias_kind":"pith_short_16","alias_value":"RJGHE5CSNYQ72KQJ","created_at":"2026-07-05T00:02:22Z"},{"alias_kind":"pith_short_8","alias_value":"RJGHE5CS","created_at":"2026-07-05T00:02:22Z"}],"graph_snapshots":[{"event_id":"sha256:6fed8a56ad9dbb93daa1d7e2a032f66537190852842e249ffdc4e7a31e4e212d","target":"graph","created_at":"2026-07-05T00:02:22Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1909.01960/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As synthetic imagery is used more frequently in training deep models, it is important to understand how different synthesis techniques impact the performance of such models. In this work, we perform a thorough evaluation of the effectiveness of several different synthesis techniques and their impact on the complexity of classifier domain adaptation to the \"real\" underlying data distribution that they seek to replicate. In addition, we propose a novel learned synthesis technique to better train classifier models than state-of-the-art offline graphical methods, while using significantly less com","authors_text":"Connor DeFanti, Jonathan Tompson, Ken Perlin, Kristofer Schlachter, Sebastian Herscher","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-04T17:38:05Z","title":"Beyond Photo Realism for Domain Adaptation from Synthetic Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.01960","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:a5d7e3fd132b17c03ddda05804304530955e77e39876459cd51008c2ee168738","target":"record","created_at":"2026-07-05T00:02:22Z","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":"95d39cf118b5e7f0b2719ab21816f214186dc9d6decf2a7c49eb217e2268c135","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-04T17:38:05Z","title_canon_sha256":"1581337e199ede26dd644ad37142aead787b9ac522892a6e7cc7e642539e9b83"},"schema_version":"1.0","source":{"id":"1909.01960","kind":"arxiv","version":1}},"canonical_sha256":"8a4c7274526e21fd2a092eb370bde2aa2543c3c7bfe1b406f8ff6e5467cec1bf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8a4c7274526e21fd2a092eb370bde2aa2543c3c7bfe1b406f8ff6e5467cec1bf","first_computed_at":"2026-07-05T00:02:22.143353Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:02:22.143353Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dFayEedMNKAuKCb3+xu5T6DAzwsQM8e9vEtpwJokCQVg+7e1aUKEmX2YjxCQlF/UmJKulWHvr2LnN22Hp7jKDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:02:22.143693Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.01960","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a5d7e3fd132b17c03ddda05804304530955e77e39876459cd51008c2ee168738","sha256:6fed8a56ad9dbb93daa1d7e2a032f66537190852842e249ffdc4e7a31e4e212d"],"state_sha256":"cc598950c95fb1635cba749792cfd1e9938dd75541b7dc1af975b54ee63efba7"}