{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:RR44JBL5LKWNF4IAIBBWQDZY3M","short_pith_number":"pith:RR44JBL5","canonical_record":{"source":{"id":"1506.05751","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-06-18T17:03:54Z","cross_cats_sorted":[],"title_canon_sha256":"158d16569c2b359a18544d4e9d882aa7dc2a291ef7d81247489bdff2c23bc22b","abstract_canon_sha256":"6da7b2408943b72a3976883489c4b0316070534c2f0768f3012420121f200917"},"schema_version":"1.0"},"canonical_sha256":"8c79c4857d5aacd2f1004043680f38db1dd52ac03b3e2e16e3d4a2079d09b78e","source":{"kind":"arxiv","id":"1506.05751","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.05751","created_at":"2026-05-18T01:44:09Z"},{"alias_kind":"arxiv_version","alias_value":"1506.05751v1","created_at":"2026-05-18T01:44:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.05751","created_at":"2026-05-18T01:44:09Z"},{"alias_kind":"pith_short_12","alias_value":"RR44JBL5LKWN","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_16","alias_value":"RR44JBL5LKWNF4IA","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_8","alias_value":"RR44JBL5","created_at":"2026-05-18T12:29:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:RR44JBL5LKWNF4IAIBBWQDZY3M","target":"record","payload":{"canonical_record":{"source":{"id":"1506.05751","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-06-18T17:03:54Z","cross_cats_sorted":[],"title_canon_sha256":"158d16569c2b359a18544d4e9d882aa7dc2a291ef7d81247489bdff2c23bc22b","abstract_canon_sha256":"6da7b2408943b72a3976883489c4b0316070534c2f0768f3012420121f200917"},"schema_version":"1.0"},"canonical_sha256":"8c79c4857d5aacd2f1004043680f38db1dd52ac03b3e2e16e3d4a2079d09b78e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:44:09.797032Z","signature_b64":"wqTgpPVIzFbTPqqPB3FMQDumPddyghErMG5HNimFyls6VKEjBk1JKT1+OR1eEMzHFp+ah5t8PzN5lVBMRcuRDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8c79c4857d5aacd2f1004043680f38db1dd52ac03b3e2e16e3d4a2079d09b78e","last_reissued_at":"2026-05-18T01:44:09.796511Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:44:09.796511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1506.05751","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:44:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P4ZqyH1MnaMerExREUY0rLCB6cogwlcIb/2XffW71KSQuhUhM8Re7NikGE9FJjPvoJRi3kQO48/nxktzwHCyAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T04:54:39.721130Z"},"content_sha256":"d967e3798fb69d300a26504598d39560c2f53d4dc317a3d65e5bc6df337db48e","schema_version":"1.0","event_id":"sha256:d967e3798fb69d300a26504598d39560c2f53d4dc317a3d65e5bc6df337db48e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:RR44JBL5LKWNF4IAIBBWQDZY3M","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arthur Szlam, Emily Denton, Rob Fergus, Soumith Chintala","submitted_at":"2015-06-18T17:03:54Z","abstract_excerpt":"In this paper we introduce a generative parametric model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion. At each level of the pyramid, a separate generative convnet model is trained using the Generative Adversarial Nets (GAN) approach (Goodfellow et al.). Samples drawn from our model are of significantly higher quality than alternate approaches. In a quantitative assessment by human evaluators, our CIFAR10 samples were mistaken for real images "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.05751","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:44:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HFabw5ECQ3x8TGLE7lEyUmhFwqY0nMAw3Jqsmy8euzpp4WFmbrr3YhQd02wmnCU0QOB/BwrPcJ8YZhiZUfVTDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T04:54:39.721824Z"},"content_sha256":"5dd7bab8dab730a4434994d4d5aced61d35f3402ee34ebd9534bcee4cdd0614f","schema_version":"1.0","event_id":"sha256:5dd7bab8dab730a4434994d4d5aced61d35f3402ee34ebd9534bcee4cdd0614f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RR44JBL5LKWNF4IAIBBWQDZY3M/bundle.json","state_url":"https://pith.science/pith/RR44JBL5LKWNF4IAIBBWQDZY3M/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RR44JBL5LKWNF4IAIBBWQDZY3M/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":"2026-06-05T04:54:39Z","links":{"resolver":"https://pith.science/pith/RR44JBL5LKWNF4IAIBBWQDZY3M","bundle":"https://pith.science/pith/RR44JBL5LKWNF4IAIBBWQDZY3M/bundle.json","state":"https://pith.science/pith/RR44JBL5LKWNF4IAIBBWQDZY3M/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RR44JBL5LKWNF4IAIBBWQDZY3M/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:RR44JBL5LKWNF4IAIBBWQDZY3M","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":"6da7b2408943b72a3976883489c4b0316070534c2f0768f3012420121f200917","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-06-18T17:03:54Z","title_canon_sha256":"158d16569c2b359a18544d4e9d882aa7dc2a291ef7d81247489bdff2c23bc22b"},"schema_version":"1.0","source":{"id":"1506.05751","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.05751","created_at":"2026-05-18T01:44:09Z"},{"alias_kind":"arxiv_version","alias_value":"1506.05751v1","created_at":"2026-05-18T01:44:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.05751","created_at":"2026-05-18T01:44:09Z"},{"alias_kind":"pith_short_12","alias_value":"RR44JBL5LKWN","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_16","alias_value":"RR44JBL5LKWNF4IA","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_8","alias_value":"RR44JBL5","created_at":"2026-05-18T12:29:39Z"}],"graph_snapshots":[{"event_id":"sha256:5dd7bab8dab730a4434994d4d5aced61d35f3402ee34ebd9534bcee4cdd0614f","target":"graph","created_at":"2026-05-18T01:44:09Z","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":"In this paper we introduce a generative parametric model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion. At each level of the pyramid, a separate generative convnet model is trained using the Generative Adversarial Nets (GAN) approach (Goodfellow et al.). Samples drawn from our model are of significantly higher quality than alternate approaches. In a quantitative assessment by human evaluators, our CIFAR10 samples were mistaken for real images ","authors_text":"Arthur Szlam, Emily Denton, Rob Fergus, Soumith Chintala","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-06-18T17:03:54Z","title":"Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.05751","kind":"arxiv","version":1},"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:d967e3798fb69d300a26504598d39560c2f53d4dc317a3d65e5bc6df337db48e","target":"record","created_at":"2026-05-18T01:44:09Z","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":"6da7b2408943b72a3976883489c4b0316070534c2f0768f3012420121f200917","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-06-18T17:03:54Z","title_canon_sha256":"158d16569c2b359a18544d4e9d882aa7dc2a291ef7d81247489bdff2c23bc22b"},"schema_version":"1.0","source":{"id":"1506.05751","kind":"arxiv","version":1}},"canonical_sha256":"8c79c4857d5aacd2f1004043680f38db1dd52ac03b3e2e16e3d4a2079d09b78e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8c79c4857d5aacd2f1004043680f38db1dd52ac03b3e2e16e3d4a2079d09b78e","first_computed_at":"2026-05-18T01:44:09.796511Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:44:09.796511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wqTgpPVIzFbTPqqPB3FMQDumPddyghErMG5HNimFyls6VKEjBk1JKT1+OR1eEMzHFp+ah5t8PzN5lVBMRcuRDA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:44:09.797032Z","signed_message":"canonical_sha256_bytes"},"source_id":"1506.05751","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d967e3798fb69d300a26504598d39560c2f53d4dc317a3d65e5bc6df337db48e","sha256:5dd7bab8dab730a4434994d4d5aced61d35f3402ee34ebd9534bcee4cdd0614f"],"state_sha256":"8923aa6d2077815490091d9662327d25af6a7a31ede7bbec90b86703384e6211"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e6A5izMXRbFaBeMJTj3rMBVK9tr5/9zYcSA91kfqfhpulYOn05UD6nOKFb9zaXnSiQmIZflsE2QJWOWB6PMFCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T04:54:39.725579Z","bundle_sha256":"a096c8b07e7bbc59d6ca2d2c9b899cd979586e874b046bd72ad781228368b2a4"}}