{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:KIUKNYTFITSJAEISGUXKS7N27J","short_pith_number":"pith:KIUKNYTF","canonical_record":{"source":{"id":"1707.04487","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-14T12:44:22Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"048aa39b42032ace7317151d4d208b6c6fd6d4a26acd4666cc2a63e625bff99c","abstract_canon_sha256":"b658bdc0dd5dd29615c8f38477bade999ea63dd5a3de9f0fc68b805559dd5267"},"schema_version":"1.0"},"canonical_sha256":"5228a6e26544e4901112352ea97dbafa5c6b0d82dc34af98e763eb7c3c8bcc44","source":{"kind":"arxiv","id":"1707.04487","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.04487","created_at":"2026-05-18T00:39:45Z"},{"alias_kind":"arxiv_version","alias_value":"1707.04487v1","created_at":"2026-05-18T00:39:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.04487","created_at":"2026-05-18T00:39:45Z"},{"alias_kind":"pith_short_12","alias_value":"KIUKNYTFITSJ","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"KIUKNYTFITSJAEIS","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"KIUKNYTF","created_at":"2026-05-18T12:31:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:KIUKNYTFITSJAEISGUXKS7N27J","target":"record","payload":{"canonical_record":{"source":{"id":"1707.04487","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-14T12:44:22Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"048aa39b42032ace7317151d4d208b6c6fd6d4a26acd4666cc2a63e625bff99c","abstract_canon_sha256":"b658bdc0dd5dd29615c8f38477bade999ea63dd5a3de9f0fc68b805559dd5267"},"schema_version":"1.0"},"canonical_sha256":"5228a6e26544e4901112352ea97dbafa5c6b0d82dc34af98e763eb7c3c8bcc44","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:45.691515Z","signature_b64":"DoviDiXIB/6fMRGaCQBkjl7lKbBjQp25+o3+Q38QxbHhLCzylqa+dpL/atk0aMhv28OBg6uZLiuKscMp0xbZDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5228a6e26544e4901112352ea97dbafa5c6b0d82dc34af98e763eb7c3c8bcc44","last_reissued_at":"2026-05-18T00:39:45.690783Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:45.690783Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1707.04487","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":"2026-05-18T00:39:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3yDp9jLsNYuA0o4NbpqIzCSM9eDW3kGYkyKYI00KoV2FTtyn9/IeK95A60x1do4Zi2HopssQeISaIr0NK5pcAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T11:27:19.091714Z"},"content_sha256":"ead4a6689cbca41029bc10bb2e03956d574729dc05b2af661a18377bdcb75e42","schema_version":"1.0","event_id":"sha256:ead4a6689cbca41029bc10bb2e03956d574729dc05b2af661a18377bdcb75e42"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:KIUKNYTFITSJAEISGUXKS7N27J","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Guiding InfoGAN with Semi-Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Adrian Spurr, Emre Aksan, Otmar Hilliges","submitted_at":"2017-07-14T12:44:22Z","abstract_excerpt":"In this paper we propose a new semi-supervised GAN architecture (ss-InfoGAN) for image synthesis that leverages information from few labels (as little as 0.22%, max. 10% of the dataset) to learn semantically meaningful and controllable data representations where latent variables correspond to label categories. The architecture builds on Information Maximizing Generative Adversarial Networks (InfoGAN) and is shown to learn both continuous and categorical codes and achieves higher quality of synthetic samples compared to fully unsupervised settings. Furthermore, we show that using small amounts "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.04487","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":"2026-05-18T00:39:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N/OIghbFQT2xlc30mzVWR7uyc/j+uvV8cWiahX231pje1wmFkmzSUjOKy/qPOlFkz3FBMaIeOkkEA5Nh7nPsDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T11:27:19.092405Z"},"content_sha256":"d0c8bd207b0808127c6d0de331b64c9decea7950b160770ed8faeca3e8432bfa","schema_version":"1.0","event_id":"sha256:d0c8bd207b0808127c6d0de331b64c9decea7950b160770ed8faeca3e8432bfa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KIUKNYTFITSJAEISGUXKS7N27J/bundle.json","state_url":"https://pith.science/pith/KIUKNYTFITSJAEISGUXKS7N27J/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KIUKNYTFITSJAEISGUXKS7N27J/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":"2026-05-27T11:27:19Z","links":{"resolver":"https://pith.science/pith/KIUKNYTFITSJAEISGUXKS7N27J","bundle":"https://pith.science/pith/KIUKNYTFITSJAEISGUXKS7N27J/bundle.json","state":"https://pith.science/pith/KIUKNYTFITSJAEISGUXKS7N27J/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KIUKNYTFITSJAEISGUXKS7N27J/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:KIUKNYTFITSJAEISGUXKS7N27J","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":"b658bdc0dd5dd29615c8f38477bade999ea63dd5a3de9f0fc68b805559dd5267","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-14T12:44:22Z","title_canon_sha256":"048aa39b42032ace7317151d4d208b6c6fd6d4a26acd4666cc2a63e625bff99c"},"schema_version":"1.0","source":{"id":"1707.04487","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.04487","created_at":"2026-05-18T00:39:45Z"},{"alias_kind":"arxiv_version","alias_value":"1707.04487v1","created_at":"2026-05-18T00:39:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.04487","created_at":"2026-05-18T00:39:45Z"},{"alias_kind":"pith_short_12","alias_value":"KIUKNYTFITSJ","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"KIUKNYTFITSJAEIS","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"KIUKNYTF","created_at":"2026-05-18T12:31:24Z"}],"graph_snapshots":[{"event_id":"sha256:d0c8bd207b0808127c6d0de331b64c9decea7950b160770ed8faeca3e8432bfa","target":"graph","created_at":"2026-05-18T00:39:45Z","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":"In this paper we propose a new semi-supervised GAN architecture (ss-InfoGAN) for image synthesis that leverages information from few labels (as little as 0.22%, max. 10% of the dataset) to learn semantically meaningful and controllable data representations where latent variables correspond to label categories. The architecture builds on Information Maximizing Generative Adversarial Networks (InfoGAN) and is shown to learn both continuous and categorical codes and achieves higher quality of synthetic samples compared to fully unsupervised settings. Furthermore, we show that using small amounts ","authors_text":"Adrian Spurr, Emre Aksan, Otmar Hilliges","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-14T12:44:22Z","title":"Guiding InfoGAN with Semi-Supervision"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.04487","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:ead4a6689cbca41029bc10bb2e03956d574729dc05b2af661a18377bdcb75e42","target":"record","created_at":"2026-05-18T00:39:45Z","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":"b658bdc0dd5dd29615c8f38477bade999ea63dd5a3de9f0fc68b805559dd5267","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-14T12:44:22Z","title_canon_sha256":"048aa39b42032ace7317151d4d208b6c6fd6d4a26acd4666cc2a63e625bff99c"},"schema_version":"1.0","source":{"id":"1707.04487","kind":"arxiv","version":1}},"canonical_sha256":"5228a6e26544e4901112352ea97dbafa5c6b0d82dc34af98e763eb7c3c8bcc44","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5228a6e26544e4901112352ea97dbafa5c6b0d82dc34af98e763eb7c3c8bcc44","first_computed_at":"2026-05-18T00:39:45.690783Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:39:45.690783Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DoviDiXIB/6fMRGaCQBkjl7lKbBjQp25+o3+Q38QxbHhLCzylqa+dpL/atk0aMhv28OBg6uZLiuKscMp0xbZDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:39:45.691515Z","signed_message":"canonical_sha256_bytes"},"source_id":"1707.04487","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ead4a6689cbca41029bc10bb2e03956d574729dc05b2af661a18377bdcb75e42","sha256:d0c8bd207b0808127c6d0de331b64c9decea7950b160770ed8faeca3e8432bfa"],"state_sha256":"08b6063ab181fca47ab2ae80dfee3a75850b405405c49239aa71d966dd5d47b6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F2Yw4lzt0vjDRfQtC6olps+mcRov2ig1h3ewR8Zq8rdMpCtvyfY/4/xrmZXcNDiZeN4LexF5lSOrVa9LZ/q6Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T11:27:19.096023Z","bundle_sha256":"29e27bcc5cb68329846272f12961b4519d5832dd4b871c451b6dab8fb5368f34"}}