{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:O3Y3S25LKPZMKJMNL25U4BE3E5","short_pith_number":"pith:O3Y3S25L","canonical_record":{"source":{"id":"1805.06173","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-16T07:48:20Z","cross_cats_sorted":[],"title_canon_sha256":"8611f759e6d1d4b62e4f07e1272eb2874b82a0dfc9cce7eac3c46a6cbf83d25c","abstract_canon_sha256":"491ddcab606af13fbd8d2f047adce071f228900d1318288aa24b0a443908f770"},"schema_version":"1.0"},"canonical_sha256":"76f1b96bab53f2c5258d5ebb4e049b275e13a55adeff7637e7f15a5acfdfb128","source":{"kind":"arxiv","id":"1805.06173","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.06173","created_at":"2026-05-18T00:15:48Z"},{"alias_kind":"arxiv_version","alias_value":"1805.06173v1","created_at":"2026-05-18T00:15:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.06173","created_at":"2026-05-18T00:15:48Z"},{"alias_kind":"pith_short_12","alias_value":"O3Y3S25LKPZM","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_16","alias_value":"O3Y3S25LKPZMKJMN","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_8","alias_value":"O3Y3S25L","created_at":"2026-05-18T12:32:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:O3Y3S25LKPZMKJMNL25U4BE3E5","target":"record","payload":{"canonical_record":{"source":{"id":"1805.06173","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-16T07:48:20Z","cross_cats_sorted":[],"title_canon_sha256":"8611f759e6d1d4b62e4f07e1272eb2874b82a0dfc9cce7eac3c46a6cbf83d25c","abstract_canon_sha256":"491ddcab606af13fbd8d2f047adce071f228900d1318288aa24b0a443908f770"},"schema_version":"1.0"},"canonical_sha256":"76f1b96bab53f2c5258d5ebb4e049b275e13a55adeff7637e7f15a5acfdfb128","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:48.567876Z","signature_b64":"QVfC0E1tvcNPJxxl+Mu2ngU47u5YBXLtTRC6YAK7wb2BRu/N8xuOptfXOg942aarubxoeDPgLL3Gz4ohHHvMDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"76f1b96bab53f2c5258d5ebb4e049b275e13a55adeff7637e7f15a5acfdfb128","last_reissued_at":"2026-05-18T00:15:48.567271Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:48.567271Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.06173","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":"2026-05-18T00:15:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"APvnbI6MUu/4CHmvPE5Ptk8esRvwvVB1sj8i65p5Kv0S2ALLha6srDUpzjPbcpuXEaR44RI8vkQlw6LNsm76BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T13:06:04.445676Z"},"content_sha256":"8e420f1f7b33e733e5369656285a26c49ab8641d0dcfbbbdc606f8359de1e980","schema_version":"1.0","event_id":"sha256:8e420f1f7b33e733e5369656285a26c49ab8641d0dcfbbbdc606f8359de1e980"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:O3Y3S25LKPZMKJMNL25U4BE3E5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Lightweight Pyramid Networks for Image Deraining","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Borong Liang, John Paisley, Xinghao Ding, Xueyang Fu, Yue Huang","submitted_at":"2018-05-16T07:48:20Z","abstract_excerpt":"Existing deep convolutional neural networks have found major success in image deraining, but at the expense of an enormous number of parameters. This limits their potential application, for example in mobile devices. In this paper, we propose a lightweight pyramid of networks (LPNet) for single image deraining. Instead of designing a complex network structures, we use domain-specific knowledge to simplify the learning process. Specifically, we find that by introducing the mature Gaussian-Laplacian image pyramid decomposition technology to the neural network, the learning problem at each pyrami"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.06173","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":"2026-05-18T00:15:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PxLvFhNUXiy7uDlngWaA0aNM7hCl+fvJNJTcqUxxSpVGVwURJHNE6rdQ0jkpmKfK23+Fw7LmgMQGEYzeCeu1CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T13:06:04.446031Z"},"content_sha256":"2126ea352d0f3a797bc884c4839f1603742e96797f8e9453c14d79629391025a","schema_version":"1.0","event_id":"sha256:2126ea352d0f3a797bc884c4839f1603742e96797f8e9453c14d79629391025a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O3Y3S25LKPZMKJMNL25U4BE3E5/bundle.json","state_url":"https://pith.science/pith/O3Y3S25LKPZMKJMNL25U4BE3E5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O3Y3S25LKPZMKJMNL25U4BE3E5/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":"2026-06-04T13:06:04Z","links":{"resolver":"https://pith.science/pith/O3Y3S25LKPZMKJMNL25U4BE3E5","bundle":"https://pith.science/pith/O3Y3S25LKPZMKJMNL25U4BE3E5/bundle.json","state":"https://pith.science/pith/O3Y3S25LKPZMKJMNL25U4BE3E5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O3Y3S25LKPZMKJMNL25U4BE3E5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:O3Y3S25LKPZMKJMNL25U4BE3E5","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":"491ddcab606af13fbd8d2f047adce071f228900d1318288aa24b0a443908f770","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-16T07:48:20Z","title_canon_sha256":"8611f759e6d1d4b62e4f07e1272eb2874b82a0dfc9cce7eac3c46a6cbf83d25c"},"schema_version":"1.0","source":{"id":"1805.06173","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.06173","created_at":"2026-05-18T00:15:48Z"},{"alias_kind":"arxiv_version","alias_value":"1805.06173v1","created_at":"2026-05-18T00:15:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.06173","created_at":"2026-05-18T00:15:48Z"},{"alias_kind":"pith_short_12","alias_value":"O3Y3S25LKPZM","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_16","alias_value":"O3Y3S25LKPZMKJMN","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_8","alias_value":"O3Y3S25L","created_at":"2026-05-18T12:32:40Z"}],"graph_snapshots":[{"event_id":"sha256:2126ea352d0f3a797bc884c4839f1603742e96797f8e9453c14d79629391025a","target":"graph","created_at":"2026-05-18T00:15:48Z","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":"Existing deep convolutional neural networks have found major success in image deraining, but at the expense of an enormous number of parameters. This limits their potential application, for example in mobile devices. In this paper, we propose a lightweight pyramid of networks (LPNet) for single image deraining. Instead of designing a complex network structures, we use domain-specific knowledge to simplify the learning process. Specifically, we find that by introducing the mature Gaussian-Laplacian image pyramid decomposition technology to the neural network, the learning problem at each pyrami","authors_text":"Borong Liang, John Paisley, Xinghao Ding, Xueyang Fu, Yue Huang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-16T07:48:20Z","title":"Lightweight Pyramid Networks for Image Deraining"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.06173","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":"sha256:8e420f1f7b33e733e5369656285a26c49ab8641d0dcfbbbdc606f8359de1e980","target":"record","created_at":"2026-05-18T00:15:48Z","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":"491ddcab606af13fbd8d2f047adce071f228900d1318288aa24b0a443908f770","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-16T07:48:20Z","title_canon_sha256":"8611f759e6d1d4b62e4f07e1272eb2874b82a0dfc9cce7eac3c46a6cbf83d25c"},"schema_version":"1.0","source":{"id":"1805.06173","kind":"arxiv","version":1}},"canonical_sha256":"76f1b96bab53f2c5258d5ebb4e049b275e13a55adeff7637e7f15a5acfdfb128","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"76f1b96bab53f2c5258d5ebb4e049b275e13a55adeff7637e7f15a5acfdfb128","first_computed_at":"2026-05-18T00:15:48.567271Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:48.567271Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QVfC0E1tvcNPJxxl+Mu2ngU47u5YBXLtTRC6YAK7wb2BRu/N8xuOptfXOg942aarubxoeDPgLL3Gz4ohHHvMDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:48.567876Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.06173","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8e420f1f7b33e733e5369656285a26c49ab8641d0dcfbbbdc606f8359de1e980","sha256:2126ea352d0f3a797bc884c4839f1603742e96797f8e9453c14d79629391025a"],"state_sha256":"2b58ec9b64af42bdc07229b51fc513426f4c733946f348675f2616aca42e1b8b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5I2kpOJfpaPSrJYlq+bpZxbgrIdWKp4gtd4+DkS2CZX18vE7RKAG1BhwN1NWtAsmjhV/u/1TxVAwLy01KYtXCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T13:06:04.448951Z","bundle_sha256":"66eb977420712e48d0f21ddf8dc3c0c47881f7f5cc9789a55c828e19bc9ba1b8"}}