{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:6JRWZPDTE6QNVQYVP4KD2M2R2Z","short_pith_number":"pith:6JRWZPDT","schema_version":"1.0","canonical_sha256":"f2636cbc7327a0dac3157f143d3351d65e737a4540b3182974efc41de26699db","source":{"kind":"arxiv","id":"1802.05701","version":1},"attestation_state":"computed","paper":{"title":"Inverting The Generator Of A Generative Adversarial Network (II)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anil A Bharath, Antonia Creswell","submitted_at":"2018-02-15T18:50:20Z","abstract_excerpt":"Generative adversarial networks (GANs) learn a deep generative model that is able to synthesise novel, high-dimensional data samples. New data samples are synthesised by passing latent samples, drawn from a chosen prior distribution, through the generative model. Once trained, the latent space exhibits interesting properties, that may be useful for down stream tasks such as classification or retrieval. Unfortunately, GANs do not offer an \"inverse model\", a mapping from data space back to latent space, making it difficult to infer a latent representation for a given data sample. In this paper, "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1802.05701","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-15T18:50:20Z","cross_cats_sorted":[],"title_canon_sha256":"f442be5c11d3c7e1717950125b0b1715271837affe5347b94ebd6f13cc7929f6","abstract_canon_sha256":"a81e982aba7c8c7a523fba16aa5045bd4543f66f2a953e30d55c055edeed7618"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:14.861484Z","signature_b64":"BsEh0DuyuI1JpqJdNFFeNTlNdOOkKoRzO080V0ISk1+CgjTfOfft+5BT7CCoh5QYB+WDdU0Kz4iPYXzmGFc3BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2636cbc7327a0dac3157f143d3351d65e737a4540b3182974efc41de26699db","last_reissued_at":"2026-05-18T00:23:14.860859Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:14.860859Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Inverting The Generator Of A Generative Adversarial Network (II)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anil A Bharath, Antonia Creswell","submitted_at":"2018-02-15T18:50:20Z","abstract_excerpt":"Generative adversarial networks (GANs) learn a deep generative model that is able to synthesise novel, high-dimensional data samples. New data samples are synthesised by passing latent samples, drawn from a chosen prior distribution, through the generative model. Once trained, the latent space exhibits interesting properties, that may be useful for down stream tasks such as classification or retrieval. Unfortunately, GANs do not offer an \"inverse model\", a mapping from data space back to latent space, making it difficult to infer a latent representation for a given data sample. In this paper, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.05701","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1802.05701","created_at":"2026-05-18T00:23:14.860945+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.05701v1","created_at":"2026-05-18T00:23:14.860945+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.05701","created_at":"2026-05-18T00:23:14.860945+00:00"},{"alias_kind":"pith_short_12","alias_value":"6JRWZPDTE6QN","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"6JRWZPDTE6QNVQYV","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"6JRWZPDT","created_at":"2026-05-18T12:32:08.215937+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6JRWZPDTE6QNVQYVP4KD2M2R2Z","json":"https://pith.science/pith/6JRWZPDTE6QNVQYVP4KD2M2R2Z.json","graph_json":"https://pith.science/api/pith-number/6JRWZPDTE6QNVQYVP4KD2M2R2Z/graph.json","events_json":"https://pith.science/api/pith-number/6JRWZPDTE6QNVQYVP4KD2M2R2Z/events.json","paper":"https://pith.science/paper/6JRWZPDT"},"agent_actions":{"view_html":"https://pith.science/pith/6JRWZPDTE6QNVQYVP4KD2M2R2Z","download_json":"https://pith.science/pith/6JRWZPDTE6QNVQYVP4KD2M2R2Z.json","view_paper":"https://pith.science/paper/6JRWZPDT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.05701&json=true","fetch_graph":"https://pith.science/api/pith-number/6JRWZPDTE6QNVQYVP4KD2M2R2Z/graph.json","fetch_events":"https://pith.science/api/pith-number/6JRWZPDTE6QNVQYVP4KD2M2R2Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6JRWZPDTE6QNVQYVP4KD2M2R2Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6JRWZPDTE6QNVQYVP4KD2M2R2Z/action/storage_attestation","attest_author":"https://pith.science/pith/6JRWZPDTE6QNVQYVP4KD2M2R2Z/action/author_attestation","sign_citation":"https://pith.science/pith/6JRWZPDTE6QNVQYVP4KD2M2R2Z/action/citation_signature","submit_replication":"https://pith.science/pith/6JRWZPDTE6QNVQYVP4KD2M2R2Z/action/replication_record"}},"created_at":"2026-05-18T00:23:14.860945+00:00","updated_at":"2026-05-18T00:23:14.860945+00:00"}