{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:546WGX6PHSSJIB62L4M4ZZ5TVT","short_pith_number":"pith:546WGX6P","canonical_record":{"source":{"id":"1808.01834","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-06T11:57:15Z","cross_cats_sorted":[],"title_canon_sha256":"8ebb49712659af5aacaf84963e225d757acc7c9316216980f72fbdfc9c051fc8","abstract_canon_sha256":"7abb83befd20554c52cc4891f1b9d114939983d22c0b0a83348e6805b11835ae"},"schema_version":"1.0"},"canonical_sha256":"ef3d635fcf3ca49407da5f19cce7b3acf2a1d39253cde7af39d69fc3830e8a93","source":{"kind":"arxiv","id":"1808.01834","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.01834","created_at":"2026-05-18T00:08:49Z"},{"alias_kind":"arxiv_version","alias_value":"1808.01834v1","created_at":"2026-05-18T00:08:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.01834","created_at":"2026-05-18T00:08:49Z"},{"alias_kind":"pith_short_12","alias_value":"546WGX6PHSSJ","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"546WGX6PHSSJIB62","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"546WGX6P","created_at":"2026-05-18T12:32:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:546WGX6PHSSJIB62L4M4ZZ5TVT","target":"record","payload":{"canonical_record":{"source":{"id":"1808.01834","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-06T11:57:15Z","cross_cats_sorted":[],"title_canon_sha256":"8ebb49712659af5aacaf84963e225d757acc7c9316216980f72fbdfc9c051fc8","abstract_canon_sha256":"7abb83befd20554c52cc4891f1b9d114939983d22c0b0a83348e6805b11835ae"},"schema_version":"1.0"},"canonical_sha256":"ef3d635fcf3ca49407da5f19cce7b3acf2a1d39253cde7af39d69fc3830e8a93","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:49.562307Z","signature_b64":"GB98JbE/7TIgvpKLwPatWWZdtGv95cVr75+Y+afFhNAUsSw0w7f8UGUXeBBzbh01eH2IKdldOYgvpcgtQh4QBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef3d635fcf3ca49407da5f19cce7b3acf2a1d39253cde7af39d69fc3830e8a93","last_reissued_at":"2026-05-18T00:08:49.561745Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:49.561745Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.01834","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":"2026-05-18T00:08:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7q82wX3/zZ/lSdqTzonNOkwqTQRV5GH3Bk3dxaUiiAgbDp771SXBnbhYcLB/amsFqTDUwRPFheVF05O23Tv+Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T22:11:26.558106Z"},"content_sha256":"369d44c0e906d49a6da319e83c8a706be7c282352a57444dbebba71aebe3d219","schema_version":"1.0","event_id":"sha256:369d44c0e906d49a6da319e83c8a706be7c282352a57444dbebba71aebe3d219"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:546WGX6PHSSJIB62L4M4ZZ5TVT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Daniel Cremers, J\\\"org St\\\"uckler, Lingni Ma, Tao Wu","submitted_at":"2018-08-06T11:57:15Z","abstract_excerpt":"Dense pixelwise prediction such as semantic segmentation is an up-to-date challenge for deep convolutional neural networks (CNNs). Many state-of-the-art approaches either tackle the loss of high-resolution information due to pooling in the encoder stage, or use dilated convolutions or high-resolution lanes to maintain detailed feature maps and predictions. Motivated by the structural analogy between multi-resolution wavelet analysis and the pooling/unpooling layers of CNNs, we introduce discrete wavelet transform (DWT) into the CNN encoder-decoder architecture and propose WCNN. The high-freque"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.01834","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":"2026-05-18T00:08:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dNTHQa0f8BvDJDWVZW2L6Gv1BbWeF+XsDmBHYsMHx987/5MtObZondIhPd9Nfkuc3ScpbqCIh/IRLEW59AXpDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T22:11:26.558812Z"},"content_sha256":"64feadb989d9dc1b30f59e1efa39d0dc8fbabad20ca6a790486460834a853056","schema_version":"1.0","event_id":"sha256:64feadb989d9dc1b30f59e1efa39d0dc8fbabad20ca6a790486460834a853056"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/546WGX6PHSSJIB62L4M4ZZ5TVT/bundle.json","state_url":"https://pith.science/pith/546WGX6PHSSJIB62L4M4ZZ5TVT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/546WGX6PHSSJIB62L4M4ZZ5TVT/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":"2026-05-25T22:11:26Z","links":{"resolver":"https://pith.science/pith/546WGX6PHSSJIB62L4M4ZZ5TVT","bundle":"https://pith.science/pith/546WGX6PHSSJIB62L4M4ZZ5TVT/bundle.json","state":"https://pith.science/pith/546WGX6PHSSJIB62L4M4ZZ5TVT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/546WGX6PHSSJIB62L4M4ZZ5TVT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:546WGX6PHSSJIB62L4M4ZZ5TVT","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":"7abb83befd20554c52cc4891f1b9d114939983d22c0b0a83348e6805b11835ae","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-06T11:57:15Z","title_canon_sha256":"8ebb49712659af5aacaf84963e225d757acc7c9316216980f72fbdfc9c051fc8"},"schema_version":"1.0","source":{"id":"1808.01834","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.01834","created_at":"2026-05-18T00:08:49Z"},{"alias_kind":"arxiv_version","alias_value":"1808.01834v1","created_at":"2026-05-18T00:08:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.01834","created_at":"2026-05-18T00:08:49Z"},{"alias_kind":"pith_short_12","alias_value":"546WGX6PHSSJ","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"546WGX6PHSSJIB62","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"546WGX6P","created_at":"2026-05-18T12:32:05Z"}],"graph_snapshots":[{"event_id":"sha256:64feadb989d9dc1b30f59e1efa39d0dc8fbabad20ca6a790486460834a853056","target":"graph","created_at":"2026-05-18T00:08:49Z","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":"Dense pixelwise prediction such as semantic segmentation is an up-to-date challenge for deep convolutional neural networks (CNNs). Many state-of-the-art approaches either tackle the loss of high-resolution information due to pooling in the encoder stage, or use dilated convolutions or high-resolution lanes to maintain detailed feature maps and predictions. Motivated by the structural analogy between multi-resolution wavelet analysis and the pooling/unpooling layers of CNNs, we introduce discrete wavelet transform (DWT) into the CNN encoder-decoder architecture and propose WCNN. The high-freque","authors_text":"Daniel Cremers, J\\\"org St\\\"uckler, Lingni Ma, Tao Wu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-06T11:57:15Z","title":"Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.01834","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":"sha256:369d44c0e906d49a6da319e83c8a706be7c282352a57444dbebba71aebe3d219","target":"record","created_at":"2026-05-18T00:08:49Z","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":"7abb83befd20554c52cc4891f1b9d114939983d22c0b0a83348e6805b11835ae","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-06T11:57:15Z","title_canon_sha256":"8ebb49712659af5aacaf84963e225d757acc7c9316216980f72fbdfc9c051fc8"},"schema_version":"1.0","source":{"id":"1808.01834","kind":"arxiv","version":1}},"canonical_sha256":"ef3d635fcf3ca49407da5f19cce7b3acf2a1d39253cde7af39d69fc3830e8a93","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ef3d635fcf3ca49407da5f19cce7b3acf2a1d39253cde7af39d69fc3830e8a93","first_computed_at":"2026-05-18T00:08:49.561745Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:08:49.561745Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GB98JbE/7TIgvpKLwPatWWZdtGv95cVr75+Y+afFhNAUsSw0w7f8UGUXeBBzbh01eH2IKdldOYgvpcgtQh4QBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:08:49.562307Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.01834","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:369d44c0e906d49a6da319e83c8a706be7c282352a57444dbebba71aebe3d219","sha256:64feadb989d9dc1b30f59e1efa39d0dc8fbabad20ca6a790486460834a853056"],"state_sha256":"6f4615743c85d37538dfda545ba369f94a0c360119572267d8fc2de0bc280436"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a368LQKl8TNN3qKtFTd5Pcmv0WdV99+s0GvsHjMbhr0NgfFXnpwokZpVZwGO/j4m2RjU1a/73DaMBk9XyBnnBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T22:11:26.562201Z","bundle_sha256":"9974ade06c795ffb310b4a9e893987728dc84e2036da72f1f5aec1919d87e59d"}}