{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:QL7IRUZ3LAEPSH6G7ZMZKSOQIS","short_pith_number":"pith:QL7IRUZ3","canonical_record":{"source":{"id":"1712.00926","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-04T06:46:41Z","cross_cats_sorted":[],"title_canon_sha256":"fba441495d816635bd6ba9a25ddc18e80f8889055aeb2ab9c45a7335b13ceca0","abstract_canon_sha256":"6bfe6d5339442fde3947dc048799949145e99683251be1f3931b27e65fb38bea"},"schema_version":"1.0"},"canonical_sha256":"82fe88d33b5808f91fc6fe599549d0449cda28fec33c023a8046c2376ea73ac9","source":{"kind":"arxiv","id":"1712.00926","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.00926","created_at":"2026-05-18T00:20:16Z"},{"alias_kind":"arxiv_version","alias_value":"1712.00926v2","created_at":"2026-05-18T00:20:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.00926","created_at":"2026-05-18T00:20:16Z"},{"alias_kind":"pith_short_12","alias_value":"QL7IRUZ3LAEP","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QL7IRUZ3LAEPSH6G","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QL7IRUZ3","created_at":"2026-05-18T12:31:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:QL7IRUZ3LAEPSH6G7ZMZKSOQIS","target":"record","payload":{"canonical_record":{"source":{"id":"1712.00926","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-04T06:46:41Z","cross_cats_sorted":[],"title_canon_sha256":"fba441495d816635bd6ba9a25ddc18e80f8889055aeb2ab9c45a7335b13ceca0","abstract_canon_sha256":"6bfe6d5339442fde3947dc048799949145e99683251be1f3931b27e65fb38bea"},"schema_version":"1.0"},"canonical_sha256":"82fe88d33b5808f91fc6fe599549d0449cda28fec33c023a8046c2376ea73ac9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:16.388502Z","signature_b64":"kCeSNaqV7ghPcip7Ed1M3MkSUJw44sLtkS3iPfItlyPpKwYZm6962WL0kFH8E0v9ubRYIqe0Zs6GZUSqY6t5Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82fe88d33b5808f91fc6fe599549d0449cda28fec33c023a8046c2376ea73ac9","last_reissued_at":"2026-05-18T00:20:16.387757Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:16.387757Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1712.00926","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":"2026-05-18T00:20:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y7LVcXUQNGpLfHhd2FaQ8aFAVHEBWqtIA8Iv1I6ZYOo+g9vuI0RSnuYUMFUdv+s72vsMnlu93Bja60uY7uS/Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T15:03:59.738470Z"},"content_sha256":"3c3211e60b385ed37a197b639f0ffcb906e0b2e542a36f010a9ab559b575751e","schema_version":"1.0","event_id":"sha256:3c3211e60b385ed37a197b639f0ffcb906e0b2e542a36f010a9ab559b575751e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:QL7IRUZ3LAEPSH6G7ZMZKSOQIS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Sampling Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bolun Cai, Dacheng Tao, Kailing Guo, Kui Jia, Xiangmin Xu","submitted_at":"2017-12-04T06:46:41Z","abstract_excerpt":"Deep convolutional neural networks achieve excellent image up-sampling performance. However, CNN-based methods tend to restore high-resolution results highly depending on traditional interpolations (e.g. bicubic). In this paper, we present a deep sampling network (DSN) for down-sampling and up-sampling without any cheap interpolation. First, the down-sampling subnetwork is trained without supervision, thereby preserving more information and producing better visual effects in the low-resolution image. Second, the up-sampling subnetwork learns a sub-pixel residual with dense connections to accel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.00926","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":"2026-05-18T00:20:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0qXH+tBtVmyZy3kytCgwzYAKXxzz/qOSxLSImmWFmTwYUJzxOQr59XT3rggriVr8Za2zbbWnkyTIp0GfEoM/AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T15:03:59.739536Z"},"content_sha256":"760205d26b8196c8f23e1690622ae2dce1e3a17904927b0e719fd33a4e185692","schema_version":"1.0","event_id":"sha256:760205d26b8196c8f23e1690622ae2dce1e3a17904927b0e719fd33a4e185692"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QL7IRUZ3LAEPSH6G7ZMZKSOQIS/bundle.json","state_url":"https://pith.science/pith/QL7IRUZ3LAEPSH6G7ZMZKSOQIS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QL7IRUZ3LAEPSH6G7ZMZKSOQIS/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":"2026-05-31T15:03:59Z","links":{"resolver":"https://pith.science/pith/QL7IRUZ3LAEPSH6G7ZMZKSOQIS","bundle":"https://pith.science/pith/QL7IRUZ3LAEPSH6G7ZMZKSOQIS/bundle.json","state":"https://pith.science/pith/QL7IRUZ3LAEPSH6G7ZMZKSOQIS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QL7IRUZ3LAEPSH6G7ZMZKSOQIS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:QL7IRUZ3LAEPSH6G7ZMZKSOQIS","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":"6bfe6d5339442fde3947dc048799949145e99683251be1f3931b27e65fb38bea","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-04T06:46:41Z","title_canon_sha256":"fba441495d816635bd6ba9a25ddc18e80f8889055aeb2ab9c45a7335b13ceca0"},"schema_version":"1.0","source":{"id":"1712.00926","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.00926","created_at":"2026-05-18T00:20:16Z"},{"alias_kind":"arxiv_version","alias_value":"1712.00926v2","created_at":"2026-05-18T00:20:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.00926","created_at":"2026-05-18T00:20:16Z"},{"alias_kind":"pith_short_12","alias_value":"QL7IRUZ3LAEP","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QL7IRUZ3LAEPSH6G","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QL7IRUZ3","created_at":"2026-05-18T12:31:39Z"}],"graph_snapshots":[{"event_id":"sha256:760205d26b8196c8f23e1690622ae2dce1e3a17904927b0e719fd33a4e185692","target":"graph","created_at":"2026-05-18T00:20:16Z","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":"Deep convolutional neural networks achieve excellent image up-sampling performance. However, CNN-based methods tend to restore high-resolution results highly depending on traditional interpolations (e.g. bicubic). In this paper, we present a deep sampling network (DSN) for down-sampling and up-sampling without any cheap interpolation. First, the down-sampling subnetwork is trained without supervision, thereby preserving more information and producing better visual effects in the low-resolution image. Second, the up-sampling subnetwork learns a sub-pixel residual with dense connections to accel","authors_text":"Bolun Cai, Dacheng Tao, Kailing Guo, Kui Jia, Xiangmin Xu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-04T06:46:41Z","title":"Deep Sampling Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.00926","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":"sha256:3c3211e60b385ed37a197b639f0ffcb906e0b2e542a36f010a9ab559b575751e","target":"record","created_at":"2026-05-18T00:20:16Z","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":"6bfe6d5339442fde3947dc048799949145e99683251be1f3931b27e65fb38bea","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-04T06:46:41Z","title_canon_sha256":"fba441495d816635bd6ba9a25ddc18e80f8889055aeb2ab9c45a7335b13ceca0"},"schema_version":"1.0","source":{"id":"1712.00926","kind":"arxiv","version":2}},"canonical_sha256":"82fe88d33b5808f91fc6fe599549d0449cda28fec33c023a8046c2376ea73ac9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"82fe88d33b5808f91fc6fe599549d0449cda28fec33c023a8046c2376ea73ac9","first_computed_at":"2026-05-18T00:20:16.387757Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:20:16.387757Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kCeSNaqV7ghPcip7Ed1M3MkSUJw44sLtkS3iPfItlyPpKwYZm6962WL0kFH8E0v9ubRYIqe0Zs6GZUSqY6t5Bw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:20:16.388502Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.00926","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3c3211e60b385ed37a197b639f0ffcb906e0b2e542a36f010a9ab559b575751e","sha256:760205d26b8196c8f23e1690622ae2dce1e3a17904927b0e719fd33a4e185692"],"state_sha256":"5ae1693a016d22384f1c8c11642c05b0036ed26d3d0ad84de7bd386cba15a321"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7fen9uYy5qTFXPaiYp9zNllg3h179N4WvHImHL2Y7TBD6TKyS7HZUas6FtJ7RfdQGBJOaKovceGcNazp+q6eBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T15:03:59.744530Z","bundle_sha256":"95a7d02bad8304612b9a78b71c29f2a2ee836556d065bea5961904b308ec1475"}}