{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:HSWH6MNFMP6IU527IN3IPEZM45","short_pith_number":"pith:HSWH6MNF","schema_version":"1.0","canonical_sha256":"3cac7f31a563fc8a775f437687932ce770b812a97b3784db099a45792c5bb063","source":{"kind":"arxiv","id":"2110.02900","version":1},"attestation_state":"computed","paper":{"title":"Meta Internal Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Lior Wolf, Raphael Bensadoun, Shir Gur, Tomer Galanti","submitted_at":"2021-10-06T16:27:38Z","abstract_excerpt":"Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image. Since these models are trained on a single image, they are limited in their scale and application. To overcome these issues, we propose a meta-learning approach that enables training over a collection of images, in order to model the internal statistics of the sample image more effectively. In the presented meta-learning approach, a single-image GAN model is generated given an input image, via a convolutional feedforward hypernetwork $f$. This network is t"},"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":"2110.02900","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-10-06T16:27:38Z","cross_cats_sorted":[],"title_canon_sha256":"c670999a7378cff3bd7b91e526aa72659477524328381040d9588d2423064a3b","abstract_canon_sha256":"fb4e1af6b9afd0c3cd90ae8d9721733a343c5a907c5cad0b20a6bb6ca930e7e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:20:30.999435Z","signature_b64":"1M1FAvirFSkhK147CFaTCcCHtNM5Hj0VcEuA5E4DKmop0dz374grrMtxjbcpBCqtMSbtE5pZFijimQIa/m6FDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3cac7f31a563fc8a775f437687932ce770b812a97b3784db099a45792c5bb063","last_reissued_at":"2026-07-05T03:20:30.999076Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:20:30.999076Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Meta Internal Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Lior Wolf, Raphael Bensadoun, Shir Gur, Tomer Galanti","submitted_at":"2021-10-06T16:27:38Z","abstract_excerpt":"Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image. Since these models are trained on a single image, they are limited in their scale and application. To overcome these issues, we propose a meta-learning approach that enables training over a collection of images, in order to model the internal statistics of the sample image more effectively. In the presented meta-learning approach, a single-image GAN model is generated given an input image, via a convolutional feedforward hypernetwork $f$. This network is t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.02900","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/2110.02900/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2110.02900","created_at":"2026-07-05T03:20:30.999138+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.02900v1","created_at":"2026-07-05T03:20:30.999138+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.02900","created_at":"2026-07-05T03:20:30.999138+00:00"},{"alias_kind":"pith_short_12","alias_value":"HSWH6MNFMP6I","created_at":"2026-07-05T03:20:30.999138+00:00"},{"alias_kind":"pith_short_16","alias_value":"HSWH6MNFMP6IU527","created_at":"2026-07-05T03:20:30.999138+00:00"},{"alias_kind":"pith_short_8","alias_value":"HSWH6MNF","created_at":"2026-07-05T03:20:30.999138+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/HSWH6MNFMP6IU527IN3IPEZM45","json":"https://pith.science/pith/HSWH6MNFMP6IU527IN3IPEZM45.json","graph_json":"https://pith.science/api/pith-number/HSWH6MNFMP6IU527IN3IPEZM45/graph.json","events_json":"https://pith.science/api/pith-number/HSWH6MNFMP6IU527IN3IPEZM45/events.json","paper":"https://pith.science/paper/HSWH6MNF"},"agent_actions":{"view_html":"https://pith.science/pith/HSWH6MNFMP6IU527IN3IPEZM45","download_json":"https://pith.science/pith/HSWH6MNFMP6IU527IN3IPEZM45.json","view_paper":"https://pith.science/paper/HSWH6MNF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.02900&json=true","fetch_graph":"https://pith.science/api/pith-number/HSWH6MNFMP6IU527IN3IPEZM45/graph.json","fetch_events":"https://pith.science/api/pith-number/HSWH6MNFMP6IU527IN3IPEZM45/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HSWH6MNFMP6IU527IN3IPEZM45/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HSWH6MNFMP6IU527IN3IPEZM45/action/storage_attestation","attest_author":"https://pith.science/pith/HSWH6MNFMP6IU527IN3IPEZM45/action/author_attestation","sign_citation":"https://pith.science/pith/HSWH6MNFMP6IU527IN3IPEZM45/action/citation_signature","submit_replication":"https://pith.science/pith/HSWH6MNFMP6IU527IN3IPEZM45/action/replication_record"}},"created_at":"2026-07-05T03:20:30.999138+00:00","updated_at":"2026-07-05T03:20:30.999138+00:00"}