{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:VHA6ZM4UYUIW343U2FJVH7SLE2","short_pith_number":"pith:VHA6ZM4U","canonical_record":{"source":{"id":"1710.00209","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-30T14:47:06Z","cross_cats_sorted":[],"title_canon_sha256":"950957e1525b413c1eae2d7dcc3816b09e75a9a498f580576e898a39fa6444d9","abstract_canon_sha256":"c4661d82fded008fe8644b44c6ace9d740ddc21fbe1719f79f03d5f40441c5f2"},"schema_version":"1.0"},"canonical_sha256":"a9c1ecb394c5116df374d15353fe4b26be54ca973efed7d43c90109caf8f70d5","source":{"kind":"arxiv","id":"1710.00209","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.00209","created_at":"2026-05-18T00:19:13Z"},{"alias_kind":"arxiv_version","alias_value":"1710.00209v2","created_at":"2026-05-18T00:19:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.00209","created_at":"2026-05-18T00:19:13Z"},{"alias_kind":"pith_short_12","alias_value":"VHA6ZM4UYUIW","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VHA6ZM4UYUIW343U","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VHA6ZM4U","created_at":"2026-05-18T12:31:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:VHA6ZM4UYUIW343U2FJVH7SLE2","target":"record","payload":{"canonical_record":{"source":{"id":"1710.00209","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-30T14:47:06Z","cross_cats_sorted":[],"title_canon_sha256":"950957e1525b413c1eae2d7dcc3816b09e75a9a498f580576e898a39fa6444d9","abstract_canon_sha256":"c4661d82fded008fe8644b44c6ace9d740ddc21fbe1719f79f03d5f40441c5f2"},"schema_version":"1.0"},"canonical_sha256":"a9c1ecb394c5116df374d15353fe4b26be54ca973efed7d43c90109caf8f70d5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:13.716260Z","signature_b64":"TEm8IPoKEujzfXLbtfY4r/c2nywrOrlOA6yhHIkxubg+/w9LqrKNKPpy4qEmrLW4iHCMHXISndWpwSddggLICQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a9c1ecb394c5116df374d15353fe4b26be54ca973efed7d43c90109caf8f70d5","last_reissued_at":"2026-05-18T00:19:13.715543Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:13.715543Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.00209","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":"2026-05-18T00:19:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Kk5xzrsEpbXgWBEpTGzx7hdUiHoCce/OCB4D4j8Kh5aooMpINwNudsTh3i47Zicj+NngRpvtGjstgpH3OyG6Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T23:10:40.798971Z"},"content_sha256":"ca6d5dce05e19835e9168de9fd368ddcd3073a024e9f0e68ff034c71065e375d","schema_version":"1.0","event_id":"sha256:ca6d5dce05e19835e9168de9fd368ddcd3073a024e9f0e68ff034c71065e375d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:VHA6ZM4UYUIW343U2FJVH7SLE2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improved Training for Self-Training by Confidence Assessments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Daniel Greenfeld, Dor Bank, Gal Hyams","submitted_at":"2017-09-30T14:47:06Z","abstract_excerpt":"It is well known that for some tasks, labeled data sets may be hard to gather. Therefore, we wished to tackle here the problem of having insufficient training data. We examined learning methods from unlabeled data after an initial training on a limited labeled data set. The suggested approach can be used as an online learning method on the unlabeled test set. In the general classification task, whenever we predict a label with high enough confidence, we treat it as a true label and train the data accordingly. For the semantic segmentation task, a classic example for an expensive data labeling "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.00209","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":"2026-05-18T00:19:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"maUVLf2w1jB8Dr/KFNHevtG/ty7KkFsIBGSiDwGBozbNBenTLtYxGIaeQM9WEaM/0BHTQvy6O1/IueCWFET0Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T23:10:40.799707Z"},"content_sha256":"dd4edc6c87747456cfe284c277a6ce2789b0d8625ec7c3f095e2e3ee4d1dbb73","schema_version":"1.0","event_id":"sha256:dd4edc6c87747456cfe284c277a6ce2789b0d8625ec7c3f095e2e3ee4d1dbb73"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VHA6ZM4UYUIW343U2FJVH7SLE2/bundle.json","state_url":"https://pith.science/pith/VHA6ZM4UYUIW343U2FJVH7SLE2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VHA6ZM4UYUIW343U2FJVH7SLE2/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":"2026-06-09T23:10:40Z","links":{"resolver":"https://pith.science/pith/VHA6ZM4UYUIW343U2FJVH7SLE2","bundle":"https://pith.science/pith/VHA6ZM4UYUIW343U2FJVH7SLE2/bundle.json","state":"https://pith.science/pith/VHA6ZM4UYUIW343U2FJVH7SLE2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VHA6ZM4UYUIW343U2FJVH7SLE2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:VHA6ZM4UYUIW343U2FJVH7SLE2","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":"c4661d82fded008fe8644b44c6ace9d740ddc21fbe1719f79f03d5f40441c5f2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-30T14:47:06Z","title_canon_sha256":"950957e1525b413c1eae2d7dcc3816b09e75a9a498f580576e898a39fa6444d9"},"schema_version":"1.0","source":{"id":"1710.00209","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.00209","created_at":"2026-05-18T00:19:13Z"},{"alias_kind":"arxiv_version","alias_value":"1710.00209v2","created_at":"2026-05-18T00:19:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.00209","created_at":"2026-05-18T00:19:13Z"},{"alias_kind":"pith_short_12","alias_value":"VHA6ZM4UYUIW","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VHA6ZM4UYUIW343U","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VHA6ZM4U","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:dd4edc6c87747456cfe284c277a6ce2789b0d8625ec7c3f095e2e3ee4d1dbb73","target":"graph","created_at":"2026-05-18T00:19:13Z","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":"It is well known that for some tasks, labeled data sets may be hard to gather. Therefore, we wished to tackle here the problem of having insufficient training data. We examined learning methods from unlabeled data after an initial training on a limited labeled data set. The suggested approach can be used as an online learning method on the unlabeled test set. In the general classification task, whenever we predict a label with high enough confidence, we treat it as a true label and train the data accordingly. For the semantic segmentation task, a classic example for an expensive data labeling ","authors_text":"Daniel Greenfeld, Dor Bank, Gal Hyams","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-30T14:47:06Z","title":"Improved Training for Self-Training by Confidence Assessments"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.00209","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:ca6d5dce05e19835e9168de9fd368ddcd3073a024e9f0e68ff034c71065e375d","target":"record","created_at":"2026-05-18T00:19:13Z","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":"c4661d82fded008fe8644b44c6ace9d740ddc21fbe1719f79f03d5f40441c5f2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-30T14:47:06Z","title_canon_sha256":"950957e1525b413c1eae2d7dcc3816b09e75a9a498f580576e898a39fa6444d9"},"schema_version":"1.0","source":{"id":"1710.00209","kind":"arxiv","version":2}},"canonical_sha256":"a9c1ecb394c5116df374d15353fe4b26be54ca973efed7d43c90109caf8f70d5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a9c1ecb394c5116df374d15353fe4b26be54ca973efed7d43c90109caf8f70d5","first_computed_at":"2026-05-18T00:19:13.715543Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:19:13.715543Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TEm8IPoKEujzfXLbtfY4r/c2nywrOrlOA6yhHIkxubg+/w9LqrKNKPpy4qEmrLW4iHCMHXISndWpwSddggLICQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:19:13.716260Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.00209","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ca6d5dce05e19835e9168de9fd368ddcd3073a024e9f0e68ff034c71065e375d","sha256:dd4edc6c87747456cfe284c277a6ce2789b0d8625ec7c3f095e2e3ee4d1dbb73"],"state_sha256":"ebee0869aef4b3650a0074df3bde938268ab7285d907e0e3364674f84b0e6923"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BI1/la6O1lCA4Oe8mGdKT0fB1ytZAkAtrsiYuzEbIIqqNScCpXCyKcGgPf8F8LHhXdQ4Djjr0dZ/yZnOeNtRDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T23:10:40.803986Z","bundle_sha256":"7a4280be434743233d360f7eb2434c0cde368fa8cfe7dc39495bb4289f695302"}}