{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:I4H2CDH3SMGGEZMYTZW5SJGF4A","short_pith_number":"pith:I4H2CDH3","canonical_record":{"source":{"id":"1705.06566","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-18T13:09:45Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5baaa95bc106ecdefbdf6a82e0f4aca95ac8508d5c2a23f909acac0f82870837","abstract_canon_sha256":"299e160d2efed8707fdf826113ae8006e5bd244ab26f7c1b93e32358fb00b610"},"schema_version":"1.0"},"canonical_sha256":"470fa10cfb930c6265989e6dd924c5e01eed18b9ca1ee3748096eb444e7620e1","source":{"kind":"arxiv","id":"1705.06566","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.06566","created_at":"2026-05-18T00:35:41Z"},{"alias_kind":"arxiv_version","alias_value":"1705.06566v2","created_at":"2026-05-18T00:35:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.06566","created_at":"2026-05-18T00:35:41Z"},{"alias_kind":"pith_short_12","alias_value":"I4H2CDH3SMGG","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"I4H2CDH3SMGGEZMY","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"I4H2CDH3","created_at":"2026-05-18T12:31:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:I4H2CDH3SMGGEZMYTZW5SJGF4A","target":"record","payload":{"canonical_record":{"source":{"id":"1705.06566","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-18T13:09:45Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5baaa95bc106ecdefbdf6a82e0f4aca95ac8508d5c2a23f909acac0f82870837","abstract_canon_sha256":"299e160d2efed8707fdf826113ae8006e5bd244ab26f7c1b93e32358fb00b610"},"schema_version":"1.0"},"canonical_sha256":"470fa10cfb930c6265989e6dd924c5e01eed18b9ca1ee3748096eb444e7620e1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:41.263698Z","signature_b64":"sjWVtwA6uQBgihxf8djRs1AdEj69nx3T4VYxF0WKtEOwkcIq/uu1DC0TUiIuWtvEE7/BJle2ZE0LmAqvOrXFDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"470fa10cfb930c6265989e6dd924c5e01eed18b9ca1ee3748096eb444e7620e1","last_reissued_at":"2026-05-18T00:35:41.262979Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:41.262979Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.06566","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:35:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"549EfARRlaGvhQ0bAJRhlH4trgMvr6Pbgd0WNTHQJ7LAVv5bAGs5H0vIGDlRW+IwOLx6xOVEKc98zCaUoOXvBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T04:58:32.479727Z"},"content_sha256":"21dc1be712f7f9a2bbfc6fde93066f983d994b2a3c6d8d248f68a3b9245a2ccc","schema_version":"1.0","event_id":"sha256:21dc1be712f7f9a2bbfc6fde93066f983d994b2a3c6d8d248f68a3b9245a2ccc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:I4H2CDH3SMGGEZMYTZW5SJGF4A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Texture Manifolds with the Periodic Spatial GAN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.CV","authors_text":"Nikolay Jetchev, Roland Vollgraf, Urs Bergmann","submitted_at":"2017-05-18T13:09:45Z","abstract_excerpt":"This paper introduces a novel approach to texture synthesis based on generative adversarial networks (GAN) (Goodfellow et al., 2014). We extend the structure of the input noise distribution by constructing tensors with different types of dimensions. We call this technique Periodic Spatial GAN (PSGAN). The PSGAN has several novel abilities which surpass the current state of the art in texture synthesis. First, we can learn multiple textures from datasets of one or more complex large images. Second, we show that the image generation with PSGANs has properties of a texture manifold: we can smooth"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.06566","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:35:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ITJNMcxhIS/B8J4pWb54LW/2Mzf23JVcdZGtKrNKH+FDW+/3c1/FdbO9VSLMXEAVgVLg8zltCjPGFZvBiBelCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T04:58:32.480136Z"},"content_sha256":"51b51e94e43dd2d1be61b42b104ea281ec48a5bd7e48b508cd5b8b46de1b4a89","schema_version":"1.0","event_id":"sha256:51b51e94e43dd2d1be61b42b104ea281ec48a5bd7e48b508cd5b8b46de1b4a89"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I4H2CDH3SMGGEZMYTZW5SJGF4A/bundle.json","state_url":"https://pith.science/pith/I4H2CDH3SMGGEZMYTZW5SJGF4A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I4H2CDH3SMGGEZMYTZW5SJGF4A/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":"2026-06-30T04:58:32Z","links":{"resolver":"https://pith.science/pith/I4H2CDH3SMGGEZMYTZW5SJGF4A","bundle":"https://pith.science/pith/I4H2CDH3SMGGEZMYTZW5SJGF4A/bundle.json","state":"https://pith.science/pith/I4H2CDH3SMGGEZMYTZW5SJGF4A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I4H2CDH3SMGGEZMYTZW5SJGF4A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:I4H2CDH3SMGGEZMYTZW5SJGF4A","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":"299e160d2efed8707fdf826113ae8006e5bd244ab26f7c1b93e32358fb00b610","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-18T13:09:45Z","title_canon_sha256":"5baaa95bc106ecdefbdf6a82e0f4aca95ac8508d5c2a23f909acac0f82870837"},"schema_version":"1.0","source":{"id":"1705.06566","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.06566","created_at":"2026-05-18T00:35:41Z"},{"alias_kind":"arxiv_version","alias_value":"1705.06566v2","created_at":"2026-05-18T00:35:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.06566","created_at":"2026-05-18T00:35:41Z"},{"alias_kind":"pith_short_12","alias_value":"I4H2CDH3SMGG","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"I4H2CDH3SMGGEZMY","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"I4H2CDH3","created_at":"2026-05-18T12:31:21Z"}],"graph_snapshots":[{"event_id":"sha256:51b51e94e43dd2d1be61b42b104ea281ec48a5bd7e48b508cd5b8b46de1b4a89","target":"graph","created_at":"2026-05-18T00:35:41Z","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":"This paper introduces a novel approach to texture synthesis based on generative adversarial networks (GAN) (Goodfellow et al., 2014). We extend the structure of the input noise distribution by constructing tensors with different types of dimensions. We call this technique Periodic Spatial GAN (PSGAN). The PSGAN has several novel abilities which surpass the current state of the art in texture synthesis. First, we can learn multiple textures from datasets of one or more complex large images. Second, we show that the image generation with PSGANs has properties of a texture manifold: we can smooth","authors_text":"Nikolay Jetchev, Roland Vollgraf, Urs Bergmann","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-18T13:09:45Z","title":"Learning Texture Manifolds with the Periodic Spatial GAN"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.06566","kind":"arxiv","version":2},"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":"sha256:21dc1be712f7f9a2bbfc6fde93066f983d994b2a3c6d8d248f68a3b9245a2ccc","target":"record","created_at":"2026-05-18T00:35:41Z","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":"299e160d2efed8707fdf826113ae8006e5bd244ab26f7c1b93e32358fb00b610","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-18T13:09:45Z","title_canon_sha256":"5baaa95bc106ecdefbdf6a82e0f4aca95ac8508d5c2a23f909acac0f82870837"},"schema_version":"1.0","source":{"id":"1705.06566","kind":"arxiv","version":2}},"canonical_sha256":"470fa10cfb930c6265989e6dd924c5e01eed18b9ca1ee3748096eb444e7620e1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"470fa10cfb930c6265989e6dd924c5e01eed18b9ca1ee3748096eb444e7620e1","first_computed_at":"2026-05-18T00:35:41.262979Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:35:41.262979Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sjWVtwA6uQBgihxf8djRs1AdEj69nx3T4VYxF0WKtEOwkcIq/uu1DC0TUiIuWtvEE7/BJle2ZE0LmAqvOrXFDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:35:41.263698Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.06566","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:21dc1be712f7f9a2bbfc6fde93066f983d994b2a3c6d8d248f68a3b9245a2ccc","sha256:51b51e94e43dd2d1be61b42b104ea281ec48a5bd7e48b508cd5b8b46de1b4a89"],"state_sha256":"725012ebb898f390bf552252639710c3a0d344180fe2b9e1557edd889f7eb00c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RDemkeTmP3LIGU83CGg0gn+ZsKVXG/mfwpR2dZ9UVesR81HnaHpW4C5FWOOHyH2tg4fH5tIHHKp4JFSx80YdCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-30T04:58:32.483039Z","bundle_sha256":"b7429fc6a8a637809010b55dca876bb09f7c658f6c2ac0fb9aa1914371eb1de2"}}