{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:S2P76GVPSSEIB3D2NZNZG2GPBI","short_pith_number":"pith:S2P76GVP","canonical_record":{"source":{"id":"1906.04681","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-11T16:27:51Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"7ad30ca4648cf53dbfb5c532e33f5e34ec96bc844ffb6328deb74014cfe3a6b5","abstract_canon_sha256":"6bd341b9e3fc5b041f51e154e101bf0316abca7f0c77ec8f238497a36a6eab1f"},"schema_version":"1.0"},"canonical_sha256":"969fff1aaf948880ec7a6e5b9368cf0a2b5e17c2cca213722304b76108a4e195","source":{"kind":"arxiv","id":"1906.04681","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.04681","created_at":"2026-05-17T23:43:22Z"},{"alias_kind":"arxiv_version","alias_value":"1906.04681v2","created_at":"2026-05-17T23:43:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04681","created_at":"2026-05-17T23:43:22Z"},{"alias_kind":"pith_short_12","alias_value":"S2P76GVPSSEI","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"S2P76GVPSSEIB3D2","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"S2P76GVP","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:S2P76GVPSSEIB3D2NZNZG2GPBI","target":"record","payload":{"canonical_record":{"source":{"id":"1906.04681","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-11T16:27:51Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"7ad30ca4648cf53dbfb5c532e33f5e34ec96bc844ffb6328deb74014cfe3a6b5","abstract_canon_sha256":"6bd341b9e3fc5b041f51e154e101bf0316abca7f0c77ec8f238497a36a6eab1f"},"schema_version":"1.0"},"canonical_sha256":"969fff1aaf948880ec7a6e5b9368cf0a2b5e17c2cca213722304b76108a4e195","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:22.774869Z","signature_b64":"2sRIXWLPmW2p4PpLUJfnWVQBspDbefHyGSDKvNyLerv5dvwIz5ijjIyAGK/xfAD1UUvd1XKYoJyIbmwFLbi0Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"969fff1aaf948880ec7a6e5b9368cf0a2b5e17c2cca213722304b76108a4e195","last_reissued_at":"2026-05-17T23:43:22.774312Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:22.774312Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.04681","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":"2026-05-17T23:43:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qSy0A0WO+eILisl0cQj64+KkzfvhpGotQ4EkIYMjukBvgNBcD4kgqGC1O9HLEB3++r7FMv/8GJuAlFXunfToCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T08:11:15.106423Z"},"content_sha256":"100e3310250f7ba3bebb3e54516956a5e87f268947608394dac9c0c47c3a2f74","schema_version":"1.0","event_id":"sha256:100e3310250f7ba3bebb3e54516956a5e87f268947608394dac9c0c47c3a2f74"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:S2P76GVPSSEIB3D2NZNZG2GPBI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"eess.IV","authors_text":"Nicol\\'o Savioli","submitted_at":"2019-06-11T16:27:51Z","abstract_excerpt":"Image compression is an essential approach for decreasing the size in bytes of the image without deteriorating the quality of it. Typically, classic algorithms are used but recently deep-learning has been successfully applied. In this work, is presented a deep super-resolution work-flow for image compression that maps low-resolution JPEG image to the high-resolution. The pipeline consists of two components: first, an encoder-decoder neural network learns how to transform the downsampling JPEG images to high resolution. Second, a combination between Generative Adversarial Networks (GANs) and re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04681","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":"2026-05-17T23:43:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ReXE9WHFyZ8qrNBvOIFesY7JCGQFFAjD3vPn/uIsZ1MrTp8KoMhDNdLormh1jVVt3lemgQRfkLfy+gsBajOmBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T08:11:15.106773Z"},"content_sha256":"24324134db955de12cdf4fd413fb1056323b7b1cfd8b235b6d2fc813fc7e58e7","schema_version":"1.0","event_id":"sha256:24324134db955de12cdf4fd413fb1056323b7b1cfd8b235b6d2fc813fc7e58e7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/S2P76GVPSSEIB3D2NZNZG2GPBI/bundle.json","state_url":"https://pith.science/pith/S2P76GVPSSEIB3D2NZNZG2GPBI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/S2P76GVPSSEIB3D2NZNZG2GPBI/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":"2026-06-03T08:11:15Z","links":{"resolver":"https://pith.science/pith/S2P76GVPSSEIB3D2NZNZG2GPBI","bundle":"https://pith.science/pith/S2P76GVPSSEIB3D2NZNZG2GPBI/bundle.json","state":"https://pith.science/pith/S2P76GVPSSEIB3D2NZNZG2GPBI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/S2P76GVPSSEIB3D2NZNZG2GPBI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:S2P76GVPSSEIB3D2NZNZG2GPBI","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":"6bd341b9e3fc5b041f51e154e101bf0316abca7f0c77ec8f238497a36a6eab1f","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-11T16:27:51Z","title_canon_sha256":"7ad30ca4648cf53dbfb5c532e33f5e34ec96bc844ffb6328deb74014cfe3a6b5"},"schema_version":"1.0","source":{"id":"1906.04681","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.04681","created_at":"2026-05-17T23:43:22Z"},{"alias_kind":"arxiv_version","alias_value":"1906.04681v2","created_at":"2026-05-17T23:43:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04681","created_at":"2026-05-17T23:43:22Z"},{"alias_kind":"pith_short_12","alias_value":"S2P76GVPSSEI","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"S2P76GVPSSEIB3D2","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"S2P76GVP","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:24324134db955de12cdf4fd413fb1056323b7b1cfd8b235b6d2fc813fc7e58e7","target":"graph","created_at":"2026-05-17T23:43:22Z","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":"Image compression is an essential approach for decreasing the size in bytes of the image without deteriorating the quality of it. Typically, classic algorithms are used but recently deep-learning has been successfully applied. In this work, is presented a deep super-resolution work-flow for image compression that maps low-resolution JPEG image to the high-resolution. The pipeline consists of two components: first, an encoder-decoder neural network learns how to transform the downsampling JPEG images to high resolution. Second, a combination between Generative Adversarial Networks (GANs) and re","authors_text":"Nicol\\'o Savioli","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-11T16:27:51Z","title":"A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04681","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":"sha256:100e3310250f7ba3bebb3e54516956a5e87f268947608394dac9c0c47c3a2f74","target":"record","created_at":"2026-05-17T23:43:22Z","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":"6bd341b9e3fc5b041f51e154e101bf0316abca7f0c77ec8f238497a36a6eab1f","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-11T16:27:51Z","title_canon_sha256":"7ad30ca4648cf53dbfb5c532e33f5e34ec96bc844ffb6328deb74014cfe3a6b5"},"schema_version":"1.0","source":{"id":"1906.04681","kind":"arxiv","version":2}},"canonical_sha256":"969fff1aaf948880ec7a6e5b9368cf0a2b5e17c2cca213722304b76108a4e195","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"969fff1aaf948880ec7a6e5b9368cf0a2b5e17c2cca213722304b76108a4e195","first_computed_at":"2026-05-17T23:43:22.774312Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:22.774312Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2sRIXWLPmW2p4PpLUJfnWVQBspDbefHyGSDKvNyLerv5dvwIz5ijjIyAGK/xfAD1UUvd1XKYoJyIbmwFLbi0Cg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:22.774869Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.04681","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:100e3310250f7ba3bebb3e54516956a5e87f268947608394dac9c0c47c3a2f74","sha256:24324134db955de12cdf4fd413fb1056323b7b1cfd8b235b6d2fc813fc7e58e7"],"state_sha256":"8b10f44fcd5ac496bf3f16a21ce63c6e82386ef0b885bcbe0f8e6f82c1baa16c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WLQ5kKt99qvKQ/9bbKVaotOS29q8YK6FE2PZ8DEbHUhJ0XO8WEOloJnIpJfHNuNrkf8wKEl3ju8HkVmcTx7sAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T08:11:15.108747Z","bundle_sha256":"0d71cb7cf7df8d8a611dee7a4b9eaa2c1fc22b16631e8093055f399b87f42491"}}