{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:3WKE5SOTCU437ES64JCI56WZN7","short_pith_number":"pith:3WKE5SOT","schema_version":"1.0","canonical_sha256":"dd944ec9d31539bf925ee2448efad96fe5769ee008adcc02427c473124762ba2","source":{"kind":"arxiv","id":"1706.07068","version":1},"attestation_state":"computed","paper":{"title":"CAN: Creative Adversarial Networks, Generating \"Art\" by Learning About Styles and Deviating from Style Norms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Ahmed Elgammal, Bingchen Liu, Marian Mazzone, Mohamed Elhoseiny","submitted_at":"2017-06-21T18:05:13Z","abstract_excerpt":"We propose a new system for generating art. The system generates art by looking at art and learning about style; and becomes creative by increasing the arousal potential of the generated art by deviating from the learned styles. We build over Generative Adversarial Networks (GAN), which have shown the ability to learn to generate novel images simulating a given distribution. We argue that such networks are limited in their ability to generate creative products in their original design. We propose modifications to its objective to make it capable of generating creative art by maximizing deviati"},"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":"1706.07068","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-06-21T18:05:13Z","cross_cats_sorted":[],"title_canon_sha256":"81b923d843600508cbe08b8e93ff8353b77fabaf8260efb401a98ff4f8f8c14b","abstract_canon_sha256":"86e3388f25b0178f1842ad9cd11fba35995f7e7f7c1e7322ec781f00f0f5e4c1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:52.733038Z","signature_b64":"PqsOomcBK57Cy+p99KpUjyMl22+UtrXFhA6WjKnxHKMo8MHrncrpFUbq1lBGbL6ZljfCpoaq91MHG30Jp+S4AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dd944ec9d31539bf925ee2448efad96fe5769ee008adcc02427c473124762ba2","last_reissued_at":"2026-05-18T00:41:52.732537Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:52.732537Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CAN: Creative Adversarial Networks, Generating \"Art\" by Learning About Styles and Deviating from Style Norms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Ahmed Elgammal, Bingchen Liu, Marian Mazzone, Mohamed Elhoseiny","submitted_at":"2017-06-21T18:05:13Z","abstract_excerpt":"We propose a new system for generating art. The system generates art by looking at art and learning about style; and becomes creative by increasing the arousal potential of the generated art by deviating from the learned styles. We build over Generative Adversarial Networks (GAN), which have shown the ability to learn to generate novel images simulating a given distribution. We argue that such networks are limited in their ability to generate creative products in their original design. We propose modifications to its objective to make it capable of generating creative art by maximizing deviati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.07068","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":"1706.07068","created_at":"2026-05-18T00:41:52.732618+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.07068v1","created_at":"2026-05-18T00:41:52.732618+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.07068","created_at":"2026-05-18T00:41:52.732618+00:00"},{"alias_kind":"pith_short_12","alias_value":"3WKE5SOTCU43","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_16","alias_value":"3WKE5SOTCU437ES6","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_8","alias_value":"3WKE5SOT","created_at":"2026-05-18T12:30:58.224056+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.16477","citing_title":"Seeking the Unfamiliar but Memorable: Conceptual Creativity as Meta-Learning","ref_index":24,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11170","citing_title":"Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data","ref_index":105,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3WKE5SOTCU437ES64JCI56WZN7","json":"https://pith.science/pith/3WKE5SOTCU437ES64JCI56WZN7.json","graph_json":"https://pith.science/api/pith-number/3WKE5SOTCU437ES64JCI56WZN7/graph.json","events_json":"https://pith.science/api/pith-number/3WKE5SOTCU437ES64JCI56WZN7/events.json","paper":"https://pith.science/paper/3WKE5SOT"},"agent_actions":{"view_html":"https://pith.science/pith/3WKE5SOTCU437ES64JCI56WZN7","download_json":"https://pith.science/pith/3WKE5SOTCU437ES64JCI56WZN7.json","view_paper":"https://pith.science/paper/3WKE5SOT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.07068&json=true","fetch_graph":"https://pith.science/api/pith-number/3WKE5SOTCU437ES64JCI56WZN7/graph.json","fetch_events":"https://pith.science/api/pith-number/3WKE5SOTCU437ES64JCI56WZN7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3WKE5SOTCU437ES64JCI56WZN7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3WKE5SOTCU437ES64JCI56WZN7/action/storage_attestation","attest_author":"https://pith.science/pith/3WKE5SOTCU437ES64JCI56WZN7/action/author_attestation","sign_citation":"https://pith.science/pith/3WKE5SOTCU437ES64JCI56WZN7/action/citation_signature","submit_replication":"https://pith.science/pith/3WKE5SOTCU437ES64JCI56WZN7/action/replication_record"}},"created_at":"2026-05-18T00:41:52.732618+00:00","updated_at":"2026-05-18T00:41:52.732618+00:00"}