{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:2DMBDMMSB6ZQIIB6XLD5DDKRFF","short_pith_number":"pith:2DMBDMMS","canonical_record":{"source":{"id":"1806.01576","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-05T09:31:19Z","cross_cats_sorted":[],"title_canon_sha256":"2dafa5fad832fdc247312893eeec51817f8764eee5206937e3dee02f182a7fa3","abstract_canon_sha256":"2baa0f4a6f5be46069887c9cfab79ddedc2d96d153334438f055fadcfe93ef1d"},"schema_version":"1.0"},"canonical_sha256":"d0d811b1920fb304203ebac7d18d51294a99f1510ae99f8d9112369a98826894","source":{"kind":"arxiv","id":"1806.01576","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.01576","created_at":"2026-05-18T00:14:12Z"},{"alias_kind":"arxiv_version","alias_value":"1806.01576v1","created_at":"2026-05-18T00:14:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.01576","created_at":"2026-05-18T00:14:12Z"},{"alias_kind":"pith_short_12","alias_value":"2DMBDMMSB6ZQ","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"2DMBDMMSB6ZQIIB6","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"2DMBDMMS","created_at":"2026-05-18T12:31:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:2DMBDMMSB6ZQIIB6XLD5DDKRFF","target":"record","payload":{"canonical_record":{"source":{"id":"1806.01576","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-05T09:31:19Z","cross_cats_sorted":[],"title_canon_sha256":"2dafa5fad832fdc247312893eeec51817f8764eee5206937e3dee02f182a7fa3","abstract_canon_sha256":"2baa0f4a6f5be46069887c9cfab79ddedc2d96d153334438f055fadcfe93ef1d"},"schema_version":"1.0"},"canonical_sha256":"d0d811b1920fb304203ebac7d18d51294a99f1510ae99f8d9112369a98826894","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:12.375165Z","signature_b64":"3KcW1sEXzphD3K4nAqm432MD5FcyTH6n/+xGclwpramtPOSCY3j0h6bY88KiqfgRM9uvtaB6zsZrmGuVhm1hBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0d811b1920fb304203ebac7d18d51294a99f1510ae99f8d9112369a98826894","last_reissued_at":"2026-05-18T00:14:12.374481Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:12.374481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.01576","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":"2026-05-18T00:14:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TkRzswl6hoyevIIAZ++guFL7wk7bpSaeXTzEi/pLRz5XNqk6hQPZlSJqoU3UuQCuwjZaxLjflg3qv/OalZzBCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T10:59:29.525769Z"},"content_sha256":"0c1ac1d3ef72aac4a863721d3ccc09730022bc77b7c1109a7c1c935b67b7754b","schema_version":"1.0","event_id":"sha256:0c1ac1d3ef72aac4a863721d3ccc09730022bc77b7c1109a7c1c935b67b7754b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:2DMBDMMSB6ZQIIB6XLD5DDKRFF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anton van den Hengel, Chunhua Shen, Lei Zhang, Lingqiao Liu, Peng Wang, Wei Wei, Yanning Zhang","submitted_at":"2018-06-05T09:31:19Z","abstract_excerpt":"Deep neural networks have achieved remarkable success in single image super-resolution (SISR). The computing and memory requirements of these methods have hindered their application to broad classes of real devices with limited computing power, however. One approach to this problem has been lightweight network architectures that bal- ance the super-resolution performance and the computation burden. In this study, we revisit this problem from an orthog- onal view, and propose a novel learning strategy to maxi- mize the pixel-wise fitting capacity of a given lightweight network architecture. Con"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.01576","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":"2026-05-18T00:14:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o2kH3XmwDbe5FY5W4TrdmFdkTY1r7ZH62FipIw6cslWOKj8jqt5p6ROX6LQ0CAVUMiNHhJeHG4scmiq7AvxjCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T10:59:29.526476Z"},"content_sha256":"27bae6ccc88af71f653d8bd0d52c09d2ca2226533e8d9469809a254711c8a9d6","schema_version":"1.0","event_id":"sha256:27bae6ccc88af71f653d8bd0d52c09d2ca2226533e8d9469809a254711c8a9d6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2DMBDMMSB6ZQIIB6XLD5DDKRFF/bundle.json","state_url":"https://pith.science/pith/2DMBDMMSB6ZQIIB6XLD5DDKRFF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2DMBDMMSB6ZQIIB6XLD5DDKRFF/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":"2026-05-25T10:59:29Z","links":{"resolver":"https://pith.science/pith/2DMBDMMSB6ZQIIB6XLD5DDKRFF","bundle":"https://pith.science/pith/2DMBDMMSB6ZQIIB6XLD5DDKRFF/bundle.json","state":"https://pith.science/pith/2DMBDMMSB6ZQIIB6XLD5DDKRFF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2DMBDMMSB6ZQIIB6XLD5DDKRFF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:2DMBDMMSB6ZQIIB6XLD5DDKRFF","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":"2baa0f4a6f5be46069887c9cfab79ddedc2d96d153334438f055fadcfe93ef1d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-05T09:31:19Z","title_canon_sha256":"2dafa5fad832fdc247312893eeec51817f8764eee5206937e3dee02f182a7fa3"},"schema_version":"1.0","source":{"id":"1806.01576","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.01576","created_at":"2026-05-18T00:14:12Z"},{"alias_kind":"arxiv_version","alias_value":"1806.01576v1","created_at":"2026-05-18T00:14:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.01576","created_at":"2026-05-18T00:14:12Z"},{"alias_kind":"pith_short_12","alias_value":"2DMBDMMSB6ZQ","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"2DMBDMMSB6ZQIIB6","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"2DMBDMMS","created_at":"2026-05-18T12:31:59Z"}],"graph_snapshots":[{"event_id":"sha256:27bae6ccc88af71f653d8bd0d52c09d2ca2226533e8d9469809a254711c8a9d6","target":"graph","created_at":"2026-05-18T00:14:12Z","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 neural networks have achieved remarkable success in single image super-resolution (SISR). The computing and memory requirements of these methods have hindered their application to broad classes of real devices with limited computing power, however. One approach to this problem has been lightweight network architectures that bal- ance the super-resolution performance and the computation burden. In this study, we revisit this problem from an orthog- onal view, and propose a novel learning strategy to maxi- mize the pixel-wise fitting capacity of a given lightweight network architecture. Con","authors_text":"Anton van den Hengel, Chunhua Shen, Lei Zhang, Lingqiao Liu, Peng Wang, Wei Wei, Yanning Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-05T09:31:19Z","title":"Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.01576","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":"sha256:0c1ac1d3ef72aac4a863721d3ccc09730022bc77b7c1109a7c1c935b67b7754b","target":"record","created_at":"2026-05-18T00:14:12Z","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":"2baa0f4a6f5be46069887c9cfab79ddedc2d96d153334438f055fadcfe93ef1d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-05T09:31:19Z","title_canon_sha256":"2dafa5fad832fdc247312893eeec51817f8764eee5206937e3dee02f182a7fa3"},"schema_version":"1.0","source":{"id":"1806.01576","kind":"arxiv","version":1}},"canonical_sha256":"d0d811b1920fb304203ebac7d18d51294a99f1510ae99f8d9112369a98826894","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d0d811b1920fb304203ebac7d18d51294a99f1510ae99f8d9112369a98826894","first_computed_at":"2026-05-18T00:14:12.374481Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:14:12.374481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3KcW1sEXzphD3K4nAqm432MD5FcyTH6n/+xGclwpramtPOSCY3j0h6bY88KiqfgRM9uvtaB6zsZrmGuVhm1hBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:14:12.375165Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.01576","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0c1ac1d3ef72aac4a863721d3ccc09730022bc77b7c1109a7c1c935b67b7754b","sha256:27bae6ccc88af71f653d8bd0d52c09d2ca2226533e8d9469809a254711c8a9d6"],"state_sha256":"79dc4855049c81a3a3efdd58df962a1e3fe818e65f051ac1727bf850385e5fdc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4e9+AA+6yVZ6Bbh1M6nKEHjQv53g7oxZzfXPsuKinpRJgT/AKtb5fbjJpOpOXum0Q7CcxVM83XlRuYhH2thHBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T10:59:29.530566Z","bundle_sha256":"be66fb429fe6bd0fc03f216dc3d9bfd3355a4b2519f4e20d810c65cffc09e0e4"}}