{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:FZBQVTTV7SQ3S5ACKY2L4NIR25","short_pith_number":"pith:FZBQVTTV","schema_version":"1.0","canonical_sha256":"2e430ace75fca1b974025634be3511d74c21ccbd40bde20a11462998b6b7c96c","source":{"kind":"arxiv","id":"1611.05203","version":2},"attestation_state":"computed","paper":{"title":"Will People Like Your Image? Learning the Aesthetic Space","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hendrik P. A. Lensch, Katharina Schwarz, Patrick Wieschollek","submitted_at":"2016-11-16T10:13:06Z","abstract_excerpt":"Rating how aesthetically pleasing an image appears is a highly complex matter and depends on a large number of different visual factors. Previous work has tackled the aesthetic rating problem by ranking on a 1-dimensional rating scale, e.g., incorporating handcrafted attributes. In this paper, we propose a rather general approach to automatically map aesthetic pleasingness with all its complexity into an \"aesthetic space\" to allow for a highly fine-grained resolution. In detail, making use of deep learning, our method directly learns an encoding of a given image into this high-dimensional feat"},"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":"1611.05203","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-16T10:13:06Z","cross_cats_sorted":[],"title_canon_sha256":"ce5f2d32bec86302a9bacf22ce5765cf5f518d388273df9e44c0651c13e87bc1","abstract_canon_sha256":"4fc1c655948eb5acf22281d9700f2eeb921bbc6a3f619b6b6cfb6a470e421a2f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:01.812461Z","signature_b64":"adLbihZ0zMrO8DdWoPgvNYJuCPBoXhiAekXrHWK3Vm4owdlw4QLhB6WoyDPmq7paZm5wOkhicaBdZCsTBMbdCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e430ace75fca1b974025634be3511d74c21ccbd40bde20a11462998b6b7c96c","last_reissued_at":"2026-05-18T00:29:01.811984Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:01.811984Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Will People Like Your Image? Learning the Aesthetic Space","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hendrik P. A. Lensch, Katharina Schwarz, Patrick Wieschollek","submitted_at":"2016-11-16T10:13:06Z","abstract_excerpt":"Rating how aesthetically pleasing an image appears is a highly complex matter and depends on a large number of different visual factors. Previous work has tackled the aesthetic rating problem by ranking on a 1-dimensional rating scale, e.g., incorporating handcrafted attributes. In this paper, we propose a rather general approach to automatically map aesthetic pleasingness with all its complexity into an \"aesthetic space\" to allow for a highly fine-grained resolution. In detail, making use of deep learning, our method directly learns an encoding of a given image into this high-dimensional feat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.05203","kind":"arxiv","version":2},"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":"1611.05203","created_at":"2026-05-18T00:29:01.812061+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.05203v2","created_at":"2026-05-18T00:29:01.812061+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.05203","created_at":"2026-05-18T00:29:01.812061+00:00"},{"alias_kind":"pith_short_12","alias_value":"FZBQVTTV7SQ3","created_at":"2026-05-18T12:30:15.759754+00:00"},{"alias_kind":"pith_short_16","alias_value":"FZBQVTTV7SQ3S5AC","created_at":"2026-05-18T12:30:15.759754+00:00"},{"alias_kind":"pith_short_8","alias_value":"FZBQVTTV","created_at":"2026-05-18T12:30:15.759754+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/FZBQVTTV7SQ3S5ACKY2L4NIR25","json":"https://pith.science/pith/FZBQVTTV7SQ3S5ACKY2L4NIR25.json","graph_json":"https://pith.science/api/pith-number/FZBQVTTV7SQ3S5ACKY2L4NIR25/graph.json","events_json":"https://pith.science/api/pith-number/FZBQVTTV7SQ3S5ACKY2L4NIR25/events.json","paper":"https://pith.science/paper/FZBQVTTV"},"agent_actions":{"view_html":"https://pith.science/pith/FZBQVTTV7SQ3S5ACKY2L4NIR25","download_json":"https://pith.science/pith/FZBQVTTV7SQ3S5ACKY2L4NIR25.json","view_paper":"https://pith.science/paper/FZBQVTTV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.05203&json=true","fetch_graph":"https://pith.science/api/pith-number/FZBQVTTV7SQ3S5ACKY2L4NIR25/graph.json","fetch_events":"https://pith.science/api/pith-number/FZBQVTTV7SQ3S5ACKY2L4NIR25/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FZBQVTTV7SQ3S5ACKY2L4NIR25/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FZBQVTTV7SQ3S5ACKY2L4NIR25/action/storage_attestation","attest_author":"https://pith.science/pith/FZBQVTTV7SQ3S5ACKY2L4NIR25/action/author_attestation","sign_citation":"https://pith.science/pith/FZBQVTTV7SQ3S5ACKY2L4NIR25/action/citation_signature","submit_replication":"https://pith.science/pith/FZBQVTTV7SQ3S5ACKY2L4NIR25/action/replication_record"}},"created_at":"2026-05-18T00:29:01.812061+00:00","updated_at":"2026-05-18T00:29:01.812061+00:00"}