{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:4QR75YJTVBHC6ERAQSUYDBYWUM","short_pith_number":"pith:4QR75YJT","canonical_record":{"source":{"id":"2002.09818","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-23T03:35:45Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"0e398def5c47ea9de21f0a7dcdb8293aed5992a48ed850fa443e6f089be3cb0f","abstract_canon_sha256":"5e9e1a615b2d1ccec8485ad3c97e2b85d82ed3719d6aa3401381dddac5ff1f81"},"schema_version":"1.0"},"canonical_sha256":"e423fee133a84e2f122084a9818716a323065ef060ee278a3d210cc230e6249f","source":{"kind":"arxiv","id":"2002.09818","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.09818","created_at":"2026-07-05T00:43:11Z"},{"alias_kind":"arxiv_version","alias_value":"2002.09818v1","created_at":"2026-07-05T00:43:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.09818","created_at":"2026-07-05T00:43:11Z"},{"alias_kind":"pith_short_12","alias_value":"4QR75YJTVBHC","created_at":"2026-07-05T00:43:11Z"},{"alias_kind":"pith_short_16","alias_value":"4QR75YJTVBHC6ERA","created_at":"2026-07-05T00:43:11Z"},{"alias_kind":"pith_short_8","alias_value":"4QR75YJT","created_at":"2026-07-05T00:43:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:4QR75YJTVBHC6ERAQSUYDBYWUM","target":"record","payload":{"canonical_record":{"source":{"id":"2002.09818","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-23T03:35:45Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"0e398def5c47ea9de21f0a7dcdb8293aed5992a48ed850fa443e6f089be3cb0f","abstract_canon_sha256":"5e9e1a615b2d1ccec8485ad3c97e2b85d82ed3719d6aa3401381dddac5ff1f81"},"schema_version":"1.0"},"canonical_sha256":"e423fee133a84e2f122084a9818716a323065ef060ee278a3d210cc230e6249f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:43:11.900152Z","signature_b64":"Lr5CDkPEC2PpTkAYPHCW27f66SnS9DNE8Cu2v5tXfVaNZjkM3Ck80msyZghL19Kwcygm/UhjC5HVTIMEoQgHBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e423fee133a84e2f122084a9818716a323065ef060ee278a3d210cc230e6249f","last_reissued_at":"2026-07-05T00:43:11.899765Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:43:11.899765Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2002.09818","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":"2026-07-05T00:43:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7v1xNj1cOR1j14QSqjQtVb02zXVVxo6Ujn9AAZNLq/QTKqe8FEB9SNKUHuaSi8AxZRnz3VPCbq0Pk+6o8Kc9BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T07:46:44.156591Z"},"content_sha256":"c6e14c49c617eb7bd30faeb739a74d6f80279a0f48b5e1c383271b109a4d7888","schema_version":"1.0","event_id":"sha256:c6e14c49c617eb7bd30faeb739a74d6f80279a0f48b5e1c383271b109a4d7888"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:4QR75YJTVBHC6ERAQSUYDBYWUM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Assembling Semantically-Disentangled Representations for Predictive-Generative Models via Adaptation from Synthetic Domain","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Burkay Donderici, Caleb New, Chenliang Xu","submitted_at":"2020-02-23T03:35:45Z","abstract_excerpt":"Deep neural networks can form high-level hierarchical representations of input data. Various researchers have demonstrated that these representations can be used to enable a variety of useful applications. However, such representations are typically based on the statistics within the data, and may not conform with the semantic representation that may be necessitated by the application. Conditional models are typically used to overcome this challenge, but they require large annotated datasets which are difficult to come by and costly to create. In this paper, we show that semantically-aligned r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.09818","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2002.09818/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T00:43:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W98H/RHJph3+hafsEitqWjNjCT3ObYybHofBn99js/sa3q/WPIDfbbGO6PzBysqcSzRQxVMptEbGtVnS1d+VDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T07:46:44.157514Z"},"content_sha256":"2bba8cb1c6300e03997786b04d9353afd77643361436272b944dcb9280a15048","schema_version":"1.0","event_id":"sha256:2bba8cb1c6300e03997786b04d9353afd77643361436272b944dcb9280a15048"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4QR75YJTVBHC6ERAQSUYDBYWUM/bundle.json","state_url":"https://pith.science/pith/4QR75YJTVBHC6ERAQSUYDBYWUM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4QR75YJTVBHC6ERAQSUYDBYWUM/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":"2026-08-20T07:46:44Z","links":{"resolver":"https://pith.science/pith/4QR75YJTVBHC6ERAQSUYDBYWUM","bundle":"https://pith.science/pith/4QR75YJTVBHC6ERAQSUYDBYWUM/bundle.json","state":"https://pith.science/pith/4QR75YJTVBHC6ERAQSUYDBYWUM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4QR75YJTVBHC6ERAQSUYDBYWUM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:4QR75YJTVBHC6ERAQSUYDBYWUM","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":"5e9e1a615b2d1ccec8485ad3c97e2b85d82ed3719d6aa3401381dddac5ff1f81","cross_cats_sorted":["cs.LG","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-23T03:35:45Z","title_canon_sha256":"0e398def5c47ea9de21f0a7dcdb8293aed5992a48ed850fa443e6f089be3cb0f"},"schema_version":"1.0","source":{"id":"2002.09818","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.09818","created_at":"2026-07-05T00:43:11Z"},{"alias_kind":"arxiv_version","alias_value":"2002.09818v1","created_at":"2026-07-05T00:43:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.09818","created_at":"2026-07-05T00:43:11Z"},{"alias_kind":"pith_short_12","alias_value":"4QR75YJTVBHC","created_at":"2026-07-05T00:43:11Z"},{"alias_kind":"pith_short_16","alias_value":"4QR75YJTVBHC6ERA","created_at":"2026-07-05T00:43:11Z"},{"alias_kind":"pith_short_8","alias_value":"4QR75YJT","created_at":"2026-07-05T00:43:11Z"}],"graph_snapshots":[{"event_id":"sha256:2bba8cb1c6300e03997786b04d9353afd77643361436272b944dcb9280a15048","target":"graph","created_at":"2026-07-05T00:43:11Z","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/2002.09818/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep neural networks can form high-level hierarchical representations of input data. Various researchers have demonstrated that these representations can be used to enable a variety of useful applications. However, such representations are typically based on the statistics within the data, and may not conform with the semantic representation that may be necessitated by the application. Conditional models are typically used to overcome this challenge, but they require large annotated datasets which are difficult to come by and costly to create. In this paper, we show that semantically-aligned r","authors_text":"Burkay Donderici, Caleb New, Chenliang Xu","cross_cats":["cs.LG","eess.IV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-23T03:35:45Z","title":"Assembling Semantically-Disentangled Representations for Predictive-Generative Models via Adaptation from Synthetic Domain"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.09818","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:c6e14c49c617eb7bd30faeb739a74d6f80279a0f48b5e1c383271b109a4d7888","target":"record","created_at":"2026-07-05T00:43:11Z","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":"5e9e1a615b2d1ccec8485ad3c97e2b85d82ed3719d6aa3401381dddac5ff1f81","cross_cats_sorted":["cs.LG","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-23T03:35:45Z","title_canon_sha256":"0e398def5c47ea9de21f0a7dcdb8293aed5992a48ed850fa443e6f089be3cb0f"},"schema_version":"1.0","source":{"id":"2002.09818","kind":"arxiv","version":1}},"canonical_sha256":"e423fee133a84e2f122084a9818716a323065ef060ee278a3d210cc230e6249f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e423fee133a84e2f122084a9818716a323065ef060ee278a3d210cc230e6249f","first_computed_at":"2026-07-05T00:43:11.899765Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:43:11.899765Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Lr5CDkPEC2PpTkAYPHCW27f66SnS9DNE8Cu2v5tXfVaNZjkM3Ck80msyZghL19Kwcygm/UhjC5HVTIMEoQgHBA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:43:11.900152Z","signed_message":"canonical_sha256_bytes"},"source_id":"2002.09818","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c6e14c49c617eb7bd30faeb739a74d6f80279a0f48b5e1c383271b109a4d7888","sha256:2bba8cb1c6300e03997786b04d9353afd77643361436272b944dcb9280a15048"],"state_sha256":"ffddb78b3ae5cb802fe24025ea2bea0a8dad910cd9cf3d0181e295fd75ef73dc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kXIE48NSC+EBOHodV3bIm99rTy9Ug+0rdZECxMwjQsX++IqqcRx9b9OUjzqEP2LcFP9kxiwdbAKftoL0sN16AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T07:46:44.163932Z","bundle_sha256":"141b9662059c3a5cd143fcbb41f99b59890632df1397416ad8e1fd7dcd1a07e0"}}