{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:EKQVG4SQFHNB3C5UMC5HWB2OLV","short_pith_number":"pith:EKQVG4SQ","canonical_record":{"source":{"id":"2003.08436","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-03-18T18:59:31Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"6f538970739f7e22ea5a4fa16883ce36bbfed664e0aa654db08760b431617dc6","abstract_canon_sha256":"26a8e2b792b285fb785572d51f77b7a3433d2d2f95902d05b66777f9303b1b27"},"schema_version":"1.0"},"canonical_sha256":"22a153725029da1d8bb460ba7b074e5d4835e1f1ce8545c2f48b00f3be05f034","source":{"kind":"arxiv","id":"2003.08436","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.08436","created_at":"2026-07-05T00:50:17Z"},{"alias_kind":"arxiv_version","alias_value":"2003.08436v2","created_at":"2026-07-05T00:50:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.08436","created_at":"2026-07-05T00:50:17Z"},{"alias_kind":"pith_short_12","alias_value":"EKQVG4SQFHNB","created_at":"2026-07-05T00:50:17Z"},{"alias_kind":"pith_short_16","alias_value":"EKQVG4SQFHNB3C5U","created_at":"2026-07-05T00:50:17Z"},{"alias_kind":"pith_short_8","alias_value":"EKQVG4SQ","created_at":"2026-07-05T00:50:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:EKQVG4SQFHNB3C5UMC5HWB2OLV","target":"record","payload":{"canonical_record":{"source":{"id":"2003.08436","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-03-18T18:59:31Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"6f538970739f7e22ea5a4fa16883ce36bbfed664e0aa654db08760b431617dc6","abstract_canon_sha256":"26a8e2b792b285fb785572d51f77b7a3433d2d2f95902d05b66777f9303b1b27"},"schema_version":"1.0"},"canonical_sha256":"22a153725029da1d8bb460ba7b074e5d4835e1f1ce8545c2f48b00f3be05f034","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:50:17.286626Z","signature_b64":"tTJTGCkBuzZouiYilcUSSZxSN9wxy9IdY5RIx546qS15uv2nSE9VO1Y1huSH4/xiTHiNUrVPmH+dGKbTbrGBCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22a153725029da1d8bb460ba7b074e5d4835e1f1ce8545c2f48b00f3be05f034","last_reissued_at":"2026-07-05T00:50:17.286103Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:50:17.286103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2003.08436","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":"2026-07-05T00:50:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3MsV86OMGGrfE8a4lNp8S5xHEbP185ZLrz1EWekdhBcWM5oATdwQlsvvj8aa5awQJIfjLOwfM0t6KJLUWf9UDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T21:14:11.907791Z"},"content_sha256":"251ff155d90ece8c84be0401a672a6f6e007007ae22ddfb5a8ef0528aa7baf6d","schema_version":"1.0","event_id":"sha256:251ff155d90ece8c84be0401a672a6f6e007007ae22ddfb5a8ef0528aa7baf6d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:EKQVG4SQFHNB3C5UMC5HWB2OLV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Collaborative Distillation for Ultra-Resolution Universal Style Transfer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Haoji Hu, Huan Wang, Ming-Hsuan Yang, Yijun Li, Yuehai Wang","submitted_at":"2020-03-18T18:59:31Z","abstract_excerpt":"Universal style transfer methods typically leverage rich representations from deep Convolutional Neural Network (CNN) models (e.g., VGG-19) pre-trained on large collections of images. Despite the effectiveness, its application is heavily constrained by the large model size to handle ultra-resolution images given limited memory. In this work, we present a new knowledge distillation method (named Collaborative Distillation) for encoder-decoder based neural style transfer to reduce the convolutional filters. The main idea is underpinned by a finding that the encoder-decoder pairs construct an exc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.08436","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/2003.08436/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":"2026-07-05T00:50:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EB1hoFBZ14O6nNPOsBD7GxYdzE+xTj/BOBc55b0K1H7+mbdDWWuzuBwAmGOu6kBY/tcD7GIGpIPw9tyJceoxBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T21:14:11.908181Z"},"content_sha256":"710c3e7ab7858f7c99554a7d448f3239217d852ff6b9a8835fc1064e24803c9c","schema_version":"1.0","event_id":"sha256:710c3e7ab7858f7c99554a7d448f3239217d852ff6b9a8835fc1064e24803c9c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EKQVG4SQFHNB3C5UMC5HWB2OLV/bundle.json","state_url":"https://pith.science/pith/EKQVG4SQFHNB3C5UMC5HWB2OLV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EKQVG4SQFHNB3C5UMC5HWB2OLV/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":"2026-08-18T21:14:11Z","links":{"resolver":"https://pith.science/pith/EKQVG4SQFHNB3C5UMC5HWB2OLV","bundle":"https://pith.science/pith/EKQVG4SQFHNB3C5UMC5HWB2OLV/bundle.json","state":"https://pith.science/pith/EKQVG4SQFHNB3C5UMC5HWB2OLV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EKQVG4SQFHNB3C5UMC5HWB2OLV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:EKQVG4SQFHNB3C5UMC5HWB2OLV","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":"26a8e2b792b285fb785572d51f77b7a3433d2d2f95902d05b66777f9303b1b27","cross_cats_sorted":["cs.LG","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-03-18T18:59:31Z","title_canon_sha256":"6f538970739f7e22ea5a4fa16883ce36bbfed664e0aa654db08760b431617dc6"},"schema_version":"1.0","source":{"id":"2003.08436","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.08436","created_at":"2026-07-05T00:50:17Z"},{"alias_kind":"arxiv_version","alias_value":"2003.08436v2","created_at":"2026-07-05T00:50:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.08436","created_at":"2026-07-05T00:50:17Z"},{"alias_kind":"pith_short_12","alias_value":"EKQVG4SQFHNB","created_at":"2026-07-05T00:50:17Z"},{"alias_kind":"pith_short_16","alias_value":"EKQVG4SQFHNB3C5U","created_at":"2026-07-05T00:50:17Z"},{"alias_kind":"pith_short_8","alias_value":"EKQVG4SQ","created_at":"2026-07-05T00:50:17Z"}],"graph_snapshots":[{"event_id":"sha256:710c3e7ab7858f7c99554a7d448f3239217d852ff6b9a8835fc1064e24803c9c","target":"graph","created_at":"2026-07-05T00:50:17Z","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/2003.08436/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Universal style transfer methods typically leverage rich representations from deep Convolutional Neural Network (CNN) models (e.g., VGG-19) pre-trained on large collections of images. Despite the effectiveness, its application is heavily constrained by the large model size to handle ultra-resolution images given limited memory. In this work, we present a new knowledge distillation method (named Collaborative Distillation) for encoder-decoder based neural style transfer to reduce the convolutional filters. The main idea is underpinned by a finding that the encoder-decoder pairs construct an exc","authors_text":"Haoji Hu, Huan Wang, Ming-Hsuan Yang, Yijun Li, Yuehai Wang","cross_cats":["cs.LG","eess.IV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-03-18T18:59:31Z","title":"Collaborative Distillation for Ultra-Resolution Universal Style Transfer"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.08436","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":"sha256:251ff155d90ece8c84be0401a672a6f6e007007ae22ddfb5a8ef0528aa7baf6d","target":"record","created_at":"2026-07-05T00:50:17Z","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":"26a8e2b792b285fb785572d51f77b7a3433d2d2f95902d05b66777f9303b1b27","cross_cats_sorted":["cs.LG","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-03-18T18:59:31Z","title_canon_sha256":"6f538970739f7e22ea5a4fa16883ce36bbfed664e0aa654db08760b431617dc6"},"schema_version":"1.0","source":{"id":"2003.08436","kind":"arxiv","version":2}},"canonical_sha256":"22a153725029da1d8bb460ba7b074e5d4835e1f1ce8545c2f48b00f3be05f034","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"22a153725029da1d8bb460ba7b074e5d4835e1f1ce8545c2f48b00f3be05f034","first_computed_at":"2026-07-05T00:50:17.286103Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:50:17.286103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tTJTGCkBuzZouiYilcUSSZxSN9wxy9IdY5RIx546qS15uv2nSE9VO1Y1huSH4/xiTHiNUrVPmH+dGKbTbrGBCw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:50:17.286626Z","signed_message":"canonical_sha256_bytes"},"source_id":"2003.08436","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:251ff155d90ece8c84be0401a672a6f6e007007ae22ddfb5a8ef0528aa7baf6d","sha256:710c3e7ab7858f7c99554a7d448f3239217d852ff6b9a8835fc1064e24803c9c"],"state_sha256":"f45e3f1533a034998d4f3c93c8024321cbb6de2b0f719974899a76e97d6de481"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OCV5rk5UDWPeiQBUqtIBKJT05iWVtTaEbHmzI+xiYMuaFQVDf2BpTBZ5PCA7XF1hIb5YlQDYrkUkzo/4q7VZBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T21:14:11.910545Z","bundle_sha256":"2a876d1e809e16dbb922bd5696db72e634787b15b0b450a4dc77a0a4f71ec00c"}}