{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:5QOY5FAUPHV2XCBLD6C5CN4VI7","short_pith_number":"pith:5QOY5FAU","canonical_record":{"source":{"id":"2412.20164","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-28T14:30:48Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"59b47e0ddc38a4e8b1cbedc4d5585be8d426680738142643c45231170f39ec43","abstract_canon_sha256":"2e37187ab652fdb86a0d4c092a473686b218503f7a3c09d99f94f26b09bd6569"},"schema_version":"1.0"},"canonical_sha256":"ec1d8e941479ebab882b1f85d1379547f6b513c2e3096802ba10c88827055900","source":{"kind":"arxiv","id":"2412.20164","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.20164","created_at":"2026-07-05T09:54:55Z"},{"alias_kind":"arxiv_version","alias_value":"2412.20164v1","created_at":"2026-07-05T09:54:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.20164","created_at":"2026-07-05T09:54:55Z"},{"alias_kind":"pith_short_12","alias_value":"5QOY5FAUPHV2","created_at":"2026-07-05T09:54:55Z"},{"alias_kind":"pith_short_16","alias_value":"5QOY5FAUPHV2XCBL","created_at":"2026-07-05T09:54:55Z"},{"alias_kind":"pith_short_8","alias_value":"5QOY5FAU","created_at":"2026-07-05T09:54:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:5QOY5FAUPHV2XCBLD6C5CN4VI7","target":"record","payload":{"canonical_record":{"source":{"id":"2412.20164","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-28T14:30:48Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"59b47e0ddc38a4e8b1cbedc4d5585be8d426680738142643c45231170f39ec43","abstract_canon_sha256":"2e37187ab652fdb86a0d4c092a473686b218503f7a3c09d99f94f26b09bd6569"},"schema_version":"1.0"},"canonical_sha256":"ec1d8e941479ebab882b1f85d1379547f6b513c2e3096802ba10c88827055900","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:55.441717Z","signature_b64":"C1AYHo7Z+PWoyv57G1ArPEoTq0UUwh+w+FycKk42Xn6XtgaBJgCZoDhH7O8rjPWlJrle+hv0FYj/jS9KVwewDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ec1d8e941479ebab882b1f85d1379547f6b513c2e3096802ba10c88827055900","last_reissued_at":"2026-07-05T09:54:55.441081Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:55.441081Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.20164","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":"2026-07-05T09:54:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qb38I3OcyuLCGS+JZsnOA2OF5M2LDs1benZdo1XBHabV9rnSGtlN58VjVpygJmzWDqPMP6wwwMQ+BhX4RqQCCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T21:47:43.899439Z"},"content_sha256":"4a65ec19fb7bf00fb6a4569f616a880676aed3376f081a24e14512d51602614f","schema_version":"1.0","event_id":"sha256:4a65ec19fb7bf00fb6a4569f616a880676aed3376f081a24e14512d51602614f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:5QOY5FAUPHV2XCBLD6C5CN4VI7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"StyleAutoEncoder for manipulating image attributes using pre-trained StyleGAN","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Andrzej Bedychaj, Jacek Tabor, Marek \\'Smieja","submitted_at":"2024-12-28T14:30:48Z","abstract_excerpt":"Deep conditional generative models are excellent tools for creating high-quality images and editing their attributes. However, training modern generative models from scratch is very expensive and requires large computational resources. In this paper, we introduce StyleAutoEncoder (StyleAE), a lightweight AutoEncoder module, which works as a plugin for pre-trained generative models and allows for manipulating the requested attributes of images. The proposed method offers a cost-effective solution for training deep generative models with limited computational resources, making it a promising tec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.20164","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/2412.20164/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":"2026-07-05T09:54:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RYZWdv9bIZ6LA7CDa7qkHf4sXkwBuaswrvUeVraEE8cJ4Wlhuv7d12CgI9em1CKRbUbqD50hgg/6rWZt6xakCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T21:47:43.899869Z"},"content_sha256":"6cddae2f39dcab2e06ae7639dbd25f460b6eb896f6aa93551f095e26e66b88c1","schema_version":"1.0","event_id":"sha256:6cddae2f39dcab2e06ae7639dbd25f460b6eb896f6aa93551f095e26e66b88c1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5QOY5FAUPHV2XCBLD6C5CN4VI7/bundle.json","state_url":"https://pith.science/pith/5QOY5FAUPHV2XCBLD6C5CN4VI7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5QOY5FAUPHV2XCBLD6C5CN4VI7/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":"2026-08-03T21:47:43Z","links":{"resolver":"https://pith.science/pith/5QOY5FAUPHV2XCBLD6C5CN4VI7","bundle":"https://pith.science/pith/5QOY5FAUPHV2XCBLD6C5CN4VI7/bundle.json","state":"https://pith.science/pith/5QOY5FAUPHV2XCBLD6C5CN4VI7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5QOY5FAUPHV2XCBLD6C5CN4VI7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:5QOY5FAUPHV2XCBLD6C5CN4VI7","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":"2e37187ab652fdb86a0d4c092a473686b218503f7a3c09d99f94f26b09bd6569","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-28T14:30:48Z","title_canon_sha256":"59b47e0ddc38a4e8b1cbedc4d5585be8d426680738142643c45231170f39ec43"},"schema_version":"1.0","source":{"id":"2412.20164","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.20164","created_at":"2026-07-05T09:54:55Z"},{"alias_kind":"arxiv_version","alias_value":"2412.20164v1","created_at":"2026-07-05T09:54:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.20164","created_at":"2026-07-05T09:54:55Z"},{"alias_kind":"pith_short_12","alias_value":"5QOY5FAUPHV2","created_at":"2026-07-05T09:54:55Z"},{"alias_kind":"pith_short_16","alias_value":"5QOY5FAUPHV2XCBL","created_at":"2026-07-05T09:54:55Z"},{"alias_kind":"pith_short_8","alias_value":"5QOY5FAU","created_at":"2026-07-05T09:54:55Z"}],"graph_snapshots":[{"event_id":"sha256:6cddae2f39dcab2e06ae7639dbd25f460b6eb896f6aa93551f095e26e66b88c1","target":"graph","created_at":"2026-07-05T09:54:55Z","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/2412.20164/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep conditional generative models are excellent tools for creating high-quality images and editing their attributes. However, training modern generative models from scratch is very expensive and requires large computational resources. In this paper, we introduce StyleAutoEncoder (StyleAE), a lightweight AutoEncoder module, which works as a plugin for pre-trained generative models and allows for manipulating the requested attributes of images. The proposed method offers a cost-effective solution for training deep generative models with limited computational resources, making it a promising tec","authors_text":"Andrzej Bedychaj, Jacek Tabor, Marek \\'Smieja","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-28T14:30:48Z","title":"StyleAutoEncoder for manipulating image attributes using pre-trained StyleGAN"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.20164","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":"sha256:4a65ec19fb7bf00fb6a4569f616a880676aed3376f081a24e14512d51602614f","target":"record","created_at":"2026-07-05T09:54:55Z","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":"2e37187ab652fdb86a0d4c092a473686b218503f7a3c09d99f94f26b09bd6569","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-28T14:30:48Z","title_canon_sha256":"59b47e0ddc38a4e8b1cbedc4d5585be8d426680738142643c45231170f39ec43"},"schema_version":"1.0","source":{"id":"2412.20164","kind":"arxiv","version":1}},"canonical_sha256":"ec1d8e941479ebab882b1f85d1379547f6b513c2e3096802ba10c88827055900","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ec1d8e941479ebab882b1f85d1379547f6b513c2e3096802ba10c88827055900","first_computed_at":"2026-07-05T09:54:55.441081Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:54:55.441081Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"C1AYHo7Z+PWoyv57G1ArPEoTq0UUwh+w+FycKk42Xn6XtgaBJgCZoDhH7O8rjPWlJrle+hv0FYj/jS9KVwewDA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:54:55.441717Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.20164","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4a65ec19fb7bf00fb6a4569f616a880676aed3376f081a24e14512d51602614f","sha256:6cddae2f39dcab2e06ae7639dbd25f460b6eb896f6aa93551f095e26e66b88c1"],"state_sha256":"4cce4b0caba167aac108111247ee87ae28014afd56847c1faf9c7dd4b7c8208c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7w2bJBvt00HSKulqaT+l3ezyMo3fbFxfNOqfLdTvb6CVMTOVm/dH9rmxNNOLe8wtWXxQ+BkewLwemtQew9iMCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T21:47:43.902332Z","bundle_sha256":"34ac9bef259e537cae22a19984326e18c38ad00631e023409e48182b2cc7a3c2"}}