{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:IAS3SFWD3XTSERZMH6NT2MM2XA","short_pith_number":"pith:IAS3SFWD","canonical_record":{"source":{"id":"1905.01164","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-02T16:15:38Z","cross_cats_sorted":[],"title_canon_sha256":"3d354f73499cc54388298ca6910d7025480a88b4cc57dc39d1af2941485da585","abstract_canon_sha256":"c32323f996090c0357ec560490c900bb1e52609c07ffd20c01c8a3544a7449d5"},"schema_version":"1.0"},"canonical_sha256":"4025b916c3dde722472c3f9b3d319ab83c6c6835ad3496bacd263b4caed9f6a8","source":{"kind":"arxiv","id":"1905.01164","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.01164","created_at":"2026-07-05T00:02:26Z"},{"alias_kind":"arxiv_version","alias_value":"1905.01164v2","created_at":"2026-07-05T00:02:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.01164","created_at":"2026-07-05T00:02:26Z"},{"alias_kind":"pith_short_12","alias_value":"IAS3SFWD3XTS","created_at":"2026-07-05T00:02:26Z"},{"alias_kind":"pith_short_16","alias_value":"IAS3SFWD3XTSERZM","created_at":"2026-07-05T00:02:26Z"},{"alias_kind":"pith_short_8","alias_value":"IAS3SFWD","created_at":"2026-07-05T00:02:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:IAS3SFWD3XTSERZMH6NT2MM2XA","target":"record","payload":{"canonical_record":{"source":{"id":"1905.01164","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-02T16:15:38Z","cross_cats_sorted":[],"title_canon_sha256":"3d354f73499cc54388298ca6910d7025480a88b4cc57dc39d1af2941485da585","abstract_canon_sha256":"c32323f996090c0357ec560490c900bb1e52609c07ffd20c01c8a3544a7449d5"},"schema_version":"1.0"},"canonical_sha256":"4025b916c3dde722472c3f9b3d319ab83c6c6835ad3496bacd263b4caed9f6a8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:02:26.308051Z","signature_b64":"dz/AIO6v3iieX56w+R81EHr2S4Gl1yuesRuhtMfTDcmcA26SKLwtSffj/RrLCu00qB0osHIYJjGINDKXmkxPDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4025b916c3dde722472c3f9b3d319ab83c6c6835ad3496bacd263b4caed9f6a8","last_reissued_at":"2026-07-05T00:02:26.307631Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:02:26.307631Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.01164","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":"2026-07-05T00:02:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bxnDWm8ZQ7CvxOZhyuqqxznDuHJcf9QkeS1+wLFXmw7eFsZMe0Uk9kJkNuAP7us582fNt6ZavT/jb0Y0V6K2Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:07:02.067265Z"},"content_sha256":"bc16cddc86df7803003e24c4880905dd57e5c7f18248b218175665b51e47c996","schema_version":"1.0","event_id":"sha256:bc16cddc86df7803003e24c4880905dd57e5c7f18248b218175665b51e47c996"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:IAS3SFWD3XTSERZMH6NT2MM2XA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SinGAN: Learning a Generative Model from a Single Natural Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Tali Dekel, Tamar Rott Shaham, Tomer Michaeli","submitted_at":"2019-05-02T16:15:38Z","abstract_excerpt":"We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image. SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. This allows generating new samples of arbitrary size and aspect ratio, that have significant variability, yet maintain both the global structure and the fine "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.01164","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1905.01164/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:02:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CVGbDEZO1B0FZmZ82nXojw6bjRR/J5qWQatYKkhfTIfpa694L31KTqhWsFCDoroa2DuHQx5by838+dXKZFd8BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:07:02.068185Z"},"content_sha256":"80c85acd73ee35c8e4fa392934786854ba5d03efabf1737a8eedec6a40c2b399","schema_version":"1.0","event_id":"sha256:80c85acd73ee35c8e4fa392934786854ba5d03efabf1737a8eedec6a40c2b399"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IAS3SFWD3XTSERZMH6NT2MM2XA/bundle.json","state_url":"https://pith.science/pith/IAS3SFWD3XTSERZMH6NT2MM2XA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IAS3SFWD3XTSERZMH6NT2MM2XA/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":"2026-08-07T23:07:02Z","links":{"resolver":"https://pith.science/pith/IAS3SFWD3XTSERZMH6NT2MM2XA","bundle":"https://pith.science/pith/IAS3SFWD3XTSERZMH6NT2MM2XA/bundle.json","state":"https://pith.science/pith/IAS3SFWD3XTSERZMH6NT2MM2XA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IAS3SFWD3XTSERZMH6NT2MM2XA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:IAS3SFWD3XTSERZMH6NT2MM2XA","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":"c32323f996090c0357ec560490c900bb1e52609c07ffd20c01c8a3544a7449d5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-02T16:15:38Z","title_canon_sha256":"3d354f73499cc54388298ca6910d7025480a88b4cc57dc39d1af2941485da585"},"schema_version":"1.0","source":{"id":"1905.01164","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.01164","created_at":"2026-07-05T00:02:26Z"},{"alias_kind":"arxiv_version","alias_value":"1905.01164v2","created_at":"2026-07-05T00:02:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.01164","created_at":"2026-07-05T00:02:26Z"},{"alias_kind":"pith_short_12","alias_value":"IAS3SFWD3XTS","created_at":"2026-07-05T00:02:26Z"},{"alias_kind":"pith_short_16","alias_value":"IAS3SFWD3XTSERZM","created_at":"2026-07-05T00:02:26Z"},{"alias_kind":"pith_short_8","alias_value":"IAS3SFWD","created_at":"2026-07-05T00:02:26Z"}],"graph_snapshots":[{"event_id":"sha256:80c85acd73ee35c8e4fa392934786854ba5d03efabf1737a8eedec6a40c2b399","target":"graph","created_at":"2026-07-05T00:02:26Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1905.01164/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image. SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. This allows generating new samples of arbitrary size and aspect ratio, that have significant variability, yet maintain both the global structure and the fine ","authors_text":"Tali Dekel, Tamar Rott Shaham, Tomer Michaeli","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-02T16:15:38Z","title":"SinGAN: Learning a Generative Model from a Single Natural Image"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.01164","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:bc16cddc86df7803003e24c4880905dd57e5c7f18248b218175665b51e47c996","target":"record","created_at":"2026-07-05T00:02:26Z","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":"c32323f996090c0357ec560490c900bb1e52609c07ffd20c01c8a3544a7449d5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-02T16:15:38Z","title_canon_sha256":"3d354f73499cc54388298ca6910d7025480a88b4cc57dc39d1af2941485da585"},"schema_version":"1.0","source":{"id":"1905.01164","kind":"arxiv","version":2}},"canonical_sha256":"4025b916c3dde722472c3f9b3d319ab83c6c6835ad3496bacd263b4caed9f6a8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4025b916c3dde722472c3f9b3d319ab83c6c6835ad3496bacd263b4caed9f6a8","first_computed_at":"2026-07-05T00:02:26.307631Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:02:26.307631Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dz/AIO6v3iieX56w+R81EHr2S4Gl1yuesRuhtMfTDcmcA26SKLwtSffj/RrLCu00qB0osHIYJjGINDKXmkxPDg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:02:26.308051Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.01164","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bc16cddc86df7803003e24c4880905dd57e5c7f18248b218175665b51e47c996","sha256:80c85acd73ee35c8e4fa392934786854ba5d03efabf1737a8eedec6a40c2b399"],"state_sha256":"364fe76a2c60cd252ec951d556c984b1007a31d37d92aeccd7795db4b4075eeb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EWbZD33Ir+6hoipjE+NVBXfQuH4mcWt6E9HLhG7gWCmpJDn++3QtiMtNOx7KlVOViP0FStbMitTDcerCL3D4Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T23:07:02.075210Z","bundle_sha256":"ebbeef3e6b7e9d2849ce669687f021293b921654d4d7afcef81b0823bbc25204"}}