{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:CWZT2MRROVA5QLFCLKQJJURTKL","short_pith_number":"pith:CWZT2MRR","canonical_record":{"source":{"id":"1806.03465","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-09T11:48:23Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"64625e8e8459f5195d5e088b43126bc37bea9249dc021c00c749db836bf32616","abstract_canon_sha256":"1664a94e9dfe8266da5763025e574b9dff80c35c1fc29e040ddf283280905967"},"schema_version":"1.0"},"canonical_sha256":"15b33d32317541d82ca25aa094d23352ffba8994327f9dcbaa5dc6932398b11f","source":{"kind":"arxiv","id":"1806.03465","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.03465","created_at":"2026-05-18T00:13:43Z"},{"alias_kind":"arxiv_version","alias_value":"1806.03465v1","created_at":"2026-05-18T00:13:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.03465","created_at":"2026-05-18T00:13:43Z"},{"alias_kind":"pith_short_12","alias_value":"CWZT2MRROVA5","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"CWZT2MRROVA5QLFC","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"CWZT2MRR","created_at":"2026-05-18T12:32:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:CWZT2MRROVA5QLFCLKQJJURTKL","target":"record","payload":{"canonical_record":{"source":{"id":"1806.03465","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-09T11:48:23Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"64625e8e8459f5195d5e088b43126bc37bea9249dc021c00c749db836bf32616","abstract_canon_sha256":"1664a94e9dfe8266da5763025e574b9dff80c35c1fc29e040ddf283280905967"},"schema_version":"1.0"},"canonical_sha256":"15b33d32317541d82ca25aa094d23352ffba8994327f9dcbaa5dc6932398b11f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:43.298411Z","signature_b64":"bH/H7+bc1CMQVOw5Q15sIJxnJ1tCTPbTgmlfUArTcbLrqS6H8wOHBuhzA2jFo9Y2gwADwnsD7K1nxFZf9oDkBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"15b33d32317541d82ca25aa094d23352ffba8994327f9dcbaa5dc6932398b11f","last_reissued_at":"2026-05-18T00:13:43.297784Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:43.297784Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.03465","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":"2026-05-18T00:13:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bo3noyTtT1Wmkt+8D+LM2t9pMx1+VyRlG19ZF0CFkJTm7DFt6xPDt4c10ys53sQJc9cji4hCyonnYo4qPWz5Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T04:58:15.468058Z"},"content_sha256":"456d2f9e8bbc6695563a34ecc23329057f1293abf1898a2809de968a97cb1267","schema_version":"1.0","event_id":"sha256:456d2f9e8bbc6695563a34ecc23329057f1293abf1898a2809de968a97cb1267"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:CWZT2MRROVA5QLFCLKQJJURTKL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Robust Semantic Segmentation with Ladder-DenseNet Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Ivan Kre\\v{s}o, Marin Or\\v{s}i\\'c, Petra Bevandi\\'c, Sini\\v{s}a \\v{S}egvi\\'c","submitted_at":"2018-06-09T11:48:23Z","abstract_excerpt":"We present semantic segmentation experiments with a model capable to perform predictions on four benchmark datasets: Cityscapes, ScanNet, WildDash and KITTI. We employ a ladder-style convolutional architecture featuring a modified DenseNet-169 model in the downsampling datapath, and only one convolution in each stage of the upsampling datapath. Due to limited computing resources, we perform the training only on Cityscapes Fine train+val, ScanNet train, WildDash val and KITTI train. We evaluate the trained model on the test subsets of the four benchmarks in concordance with the guidelines of th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.03465","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":"2026-05-18T00:13:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MN1jyOA/Wb6mzEjUnyeyzBy/9XdVYYIX+oIOvzQFfIm0SzanLrQS2xI9eLW5QCZXic6j9MTyQyN13zkTRQqFBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T04:58:15.468692Z"},"content_sha256":"c9e8e23f5194525f076116e1b7925652b02c369dda955cddd779c777556d9a7e","schema_version":"1.0","event_id":"sha256:c9e8e23f5194525f076116e1b7925652b02c369dda955cddd779c777556d9a7e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CWZT2MRROVA5QLFCLKQJJURTKL/bundle.json","state_url":"https://pith.science/pith/CWZT2MRROVA5QLFCLKQJJURTKL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CWZT2MRROVA5QLFCLKQJJURTKL/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":"2026-05-26T04:58:15Z","links":{"resolver":"https://pith.science/pith/CWZT2MRROVA5QLFCLKQJJURTKL","bundle":"https://pith.science/pith/CWZT2MRROVA5QLFCLKQJJURTKL/bundle.json","state":"https://pith.science/pith/CWZT2MRROVA5QLFCLKQJJURTKL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CWZT2MRROVA5QLFCLKQJJURTKL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:CWZT2MRROVA5QLFCLKQJJURTKL","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":"1664a94e9dfe8266da5763025e574b9dff80c35c1fc29e040ddf283280905967","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-09T11:48:23Z","title_canon_sha256":"64625e8e8459f5195d5e088b43126bc37bea9249dc021c00c749db836bf32616"},"schema_version":"1.0","source":{"id":"1806.03465","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.03465","created_at":"2026-05-18T00:13:43Z"},{"alias_kind":"arxiv_version","alias_value":"1806.03465v1","created_at":"2026-05-18T00:13:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.03465","created_at":"2026-05-18T00:13:43Z"},{"alias_kind":"pith_short_12","alias_value":"CWZT2MRROVA5","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"CWZT2MRROVA5QLFC","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"CWZT2MRR","created_at":"2026-05-18T12:32:19Z"}],"graph_snapshots":[{"event_id":"sha256:c9e8e23f5194525f076116e1b7925652b02c369dda955cddd779c777556d9a7e","target":"graph","created_at":"2026-05-18T00:13:43Z","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":"We present semantic segmentation experiments with a model capable to perform predictions on four benchmark datasets: Cityscapes, ScanNet, WildDash and KITTI. We employ a ladder-style convolutional architecture featuring a modified DenseNet-169 model in the downsampling datapath, and only one convolution in each stage of the upsampling datapath. Due to limited computing resources, we perform the training only on Cityscapes Fine train+val, ScanNet train, WildDash val and KITTI train. We evaluate the trained model on the test subsets of the four benchmarks in concordance with the guidelines of th","authors_text":"Ivan Kre\\v{s}o, Marin Or\\v{s}i\\'c, Petra Bevandi\\'c, Sini\\v{s}a \\v{S}egvi\\'c","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-09T11:48:23Z","title":"Robust Semantic Segmentation with Ladder-DenseNet Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.03465","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":"sha256:456d2f9e8bbc6695563a34ecc23329057f1293abf1898a2809de968a97cb1267","target":"record","created_at":"2026-05-18T00:13:43Z","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":"1664a94e9dfe8266da5763025e574b9dff80c35c1fc29e040ddf283280905967","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-09T11:48:23Z","title_canon_sha256":"64625e8e8459f5195d5e088b43126bc37bea9249dc021c00c749db836bf32616"},"schema_version":"1.0","source":{"id":"1806.03465","kind":"arxiv","version":1}},"canonical_sha256":"15b33d32317541d82ca25aa094d23352ffba8994327f9dcbaa5dc6932398b11f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"15b33d32317541d82ca25aa094d23352ffba8994327f9dcbaa5dc6932398b11f","first_computed_at":"2026-05-18T00:13:43.297784Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:13:43.297784Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bH/H7+bc1CMQVOw5Q15sIJxnJ1tCTPbTgmlfUArTcbLrqS6H8wOHBuhzA2jFo9Y2gwADwnsD7K1nxFZf9oDkBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:13:43.298411Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.03465","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:456d2f9e8bbc6695563a34ecc23329057f1293abf1898a2809de968a97cb1267","sha256:c9e8e23f5194525f076116e1b7925652b02c369dda955cddd779c777556d9a7e"],"state_sha256":"1792ae98608a3cc81db1a555d99ce822e90a09677cc4f888252f03d6ac49a79a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/IQIsVY/AzoOeKIRebEt109XBe0Y2DDU7EXud5OitKUREdfw5XsjtQHLuL+5rbO9PLKMy1ZNbsn/FioxUZzkAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T04:58:15.471944Z","bundle_sha256":"849e0ebb125961b1439e466e3c04e81264889248b2585a3a624f38ec1375beea"}}