{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:VT6YGX43KOV4VWRQJCYBZYZS7Y","short_pith_number":"pith:VT6YGX43","canonical_record":{"source":{"id":"1603.08155","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-03-27T01:04:27Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8d0e1bb0a59161a9cf63a3c0181915ff39941dbf0bf5ad270b7825e9dd2f8656","abstract_canon_sha256":"dc6b483bc70828dcc4087376a468d84190fb873c535da125604ca4184122aa40"},"schema_version":"1.0"},"canonical_sha256":"acfd835f9b53abcada3048b01ce332fe0db91a33b1d6568251a35d12e060279a","source":{"kind":"arxiv","id":"1603.08155","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.08155","created_at":"2026-05-18T01:18:13Z"},{"alias_kind":"arxiv_version","alias_value":"1603.08155v1","created_at":"2026-05-18T01:18:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.08155","created_at":"2026-05-18T01:18:13Z"},{"alias_kind":"pith_short_12","alias_value":"VT6YGX43KOV4","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_16","alias_value":"VT6YGX43KOV4VWRQ","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_8","alias_value":"VT6YGX43","created_at":"2026-05-18T12:30:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:VT6YGX43KOV4VWRQJCYBZYZS7Y","target":"record","payload":{"canonical_record":{"source":{"id":"1603.08155","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-03-27T01:04:27Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8d0e1bb0a59161a9cf63a3c0181915ff39941dbf0bf5ad270b7825e9dd2f8656","abstract_canon_sha256":"dc6b483bc70828dcc4087376a468d84190fb873c535da125604ca4184122aa40"},"schema_version":"1.0"},"canonical_sha256":"acfd835f9b53abcada3048b01ce332fe0db91a33b1d6568251a35d12e060279a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:18:13.083354Z","signature_b64":"G58RIRi5F2KaG2rSyGl8UIEHrCbMfY7bzgsYInOfobhEXrpxuNnU/8yqsunMzeuB6a8aGBVfF3PqpUZe2kkgBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"acfd835f9b53abcada3048b01ce332fe0db91a33b1d6568251a35d12e060279a","last_reissued_at":"2026-05-18T01:18:13.082632Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:18:13.082632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1603.08155","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":"2026-05-18T01:18:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/fmpi769q1x2dETizc/eed0Ho9FCxJWLl1POClw0JnsHKL+jxyAV+Mv8mbad6JbxoZ3S5Hj41paye0bctH7cBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T07:53:53.964441Z"},"content_sha256":"d7d848b0ed34d7c341f9d9d162fc57241f2ec065c4e0494627c17889db2748ba","schema_version":"1.0","event_id":"sha256:d7d848b0ed34d7c341f9d9d162fc57241f2ec065c4e0494627c17889db2748ba"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:VT6YGX43KOV4VWRQJCYBZYZS7Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Perceptual Losses for Real-Time Style Transfer and Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Alexandre Alahi, Justin Johnson, Li Fei-Fei","submitted_at":"2016-03-27T01:04:27Z","abstract_excerpt":"We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a \\emph{per-pixel} loss between the output and ground-truth images. Parallel work has shown that high-quality images can be generated by defining and optimizing \\emph{perceptual} loss functions based on high-level features extracted from pretrained networks. We combine the benefits of both approaches, and propose the use of perceptual loss functions for training feed-forward networks for image tran"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.08155","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":"2026-05-18T01:18:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wtP8C8aSEGpgFdG0J2/T5+uymFlYAXTJqi9xhFuNkoy52XeIiDRimcnyRl5SF6V4KcYUSLip0SaimHfeP7EUBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T07:53:53.964805Z"},"content_sha256":"a2a5c4a019a40005ce5001cf614c5dd3d2741b18a23508f5e2a578e7aad00b21","schema_version":"1.0","event_id":"sha256:a2a5c4a019a40005ce5001cf614c5dd3d2741b18a23508f5e2a578e7aad00b21"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VT6YGX43KOV4VWRQJCYBZYZS7Y/bundle.json","state_url":"https://pith.science/pith/VT6YGX43KOV4VWRQJCYBZYZS7Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VT6YGX43KOV4VWRQJCYBZYZS7Y/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":"2026-05-28T07:53:53Z","links":{"resolver":"https://pith.science/pith/VT6YGX43KOV4VWRQJCYBZYZS7Y","bundle":"https://pith.science/pith/VT6YGX43KOV4VWRQJCYBZYZS7Y/bundle.json","state":"https://pith.science/pith/VT6YGX43KOV4VWRQJCYBZYZS7Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VT6YGX43KOV4VWRQJCYBZYZS7Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:VT6YGX43KOV4VWRQJCYBZYZS7Y","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":"dc6b483bc70828dcc4087376a468d84190fb873c535da125604ca4184122aa40","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-03-27T01:04:27Z","title_canon_sha256":"8d0e1bb0a59161a9cf63a3c0181915ff39941dbf0bf5ad270b7825e9dd2f8656"},"schema_version":"1.0","source":{"id":"1603.08155","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.08155","created_at":"2026-05-18T01:18:13Z"},{"alias_kind":"arxiv_version","alias_value":"1603.08155v1","created_at":"2026-05-18T01:18:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.08155","created_at":"2026-05-18T01:18:13Z"},{"alias_kind":"pith_short_12","alias_value":"VT6YGX43KOV4","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_16","alias_value":"VT6YGX43KOV4VWRQ","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_8","alias_value":"VT6YGX43","created_at":"2026-05-18T12:30:48Z"}],"graph_snapshots":[{"event_id":"sha256:a2a5c4a019a40005ce5001cf614c5dd3d2741b18a23508f5e2a578e7aad00b21","target":"graph","created_at":"2026-05-18T01:18:13Z","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":"We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a \\emph{per-pixel} loss between the output and ground-truth images. Parallel work has shown that high-quality images can be generated by defining and optimizing \\emph{perceptual} loss functions based on high-level features extracted from pretrained networks. We combine the benefits of both approaches, and propose the use of perceptual loss functions for training feed-forward networks for image tran","authors_text":"Alexandre Alahi, Justin Johnson, Li Fei-Fei","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-03-27T01:04:27Z","title":"Perceptual Losses for Real-Time Style Transfer and Super-Resolution"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.08155","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:d7d848b0ed34d7c341f9d9d162fc57241f2ec065c4e0494627c17889db2748ba","target":"record","created_at":"2026-05-18T01:18:13Z","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":"dc6b483bc70828dcc4087376a468d84190fb873c535da125604ca4184122aa40","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-03-27T01:04:27Z","title_canon_sha256":"8d0e1bb0a59161a9cf63a3c0181915ff39941dbf0bf5ad270b7825e9dd2f8656"},"schema_version":"1.0","source":{"id":"1603.08155","kind":"arxiv","version":1}},"canonical_sha256":"acfd835f9b53abcada3048b01ce332fe0db91a33b1d6568251a35d12e060279a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"acfd835f9b53abcada3048b01ce332fe0db91a33b1d6568251a35d12e060279a","first_computed_at":"2026-05-18T01:18:13.082632Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:18:13.082632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"G58RIRi5F2KaG2rSyGl8UIEHrCbMfY7bzgsYInOfobhEXrpxuNnU/8yqsunMzeuB6a8aGBVfF3PqpUZe2kkgBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:18:13.083354Z","signed_message":"canonical_sha256_bytes"},"source_id":"1603.08155","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d7d848b0ed34d7c341f9d9d162fc57241f2ec065c4e0494627c17889db2748ba","sha256:a2a5c4a019a40005ce5001cf614c5dd3d2741b18a23508f5e2a578e7aad00b21"],"state_sha256":"ad4eb4d99e30d6240edc91a7cae4412e75719ba955f955394fa67685e6b5c551"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H5755o1+O8NAUsQvhTP6enNI28gErhC7FjK04uam8zn5C87livDIjqz1XAmALoU7fKCc2hQH20soy8XVb2sjAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T07:53:53.966809Z","bundle_sha256":"159abe12ef8d67d01f2730a9fe7b1976ee439d22fff28b5f452277f8b24de20c"}}