{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:PGSMRNUFKFLFM4AI7BX6BS6V2J","short_pith_number":"pith:PGSMRNUF","schema_version":"1.0","canonical_sha256":"79a4c8b6855156567008f86fe0cbd5d2793c7e0965992860fde361b2bd3d334e","source":{"kind":"arxiv","id":"1805.06660","version":1},"attestation_state":"computed","paper":{"title":"Single Shot Active Learning using Pseudo Annotators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"stat.ML","authors_text":"Marco Loog, Yazhou Yang","submitted_at":"2018-05-17T09:05:28Z","abstract_excerpt":"Standard myopic active learning assumes that human annotations are always obtainable whenever new samples are selected. This, however, is unrealistic in many real-world applications where human experts are not readily available at all times. In this paper, we consider the single shot setting: all the required samples should be chosen in a single shot and no human annotation can be exploited during the selection process. We propose a new method, Active Learning through Random Labeling (ALRL), which substitutes single human annotator for multiple, what we will refer to as, pseudo annotators. The"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1805.06660","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-17T09:05:28Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"806a7fbcafa43ea4cbe517b7abd4a3db5ce3d9ae6a721d5a9137d09142ded943","abstract_canon_sha256":"17021603f4ccf3e5446f3852313a4d92d7ed0d0ebf59008205506c1e1e22bc89"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:43.737784Z","signature_b64":"6dTJVsJXSIoK9K9GY4c6vLrs0XveI3rfsDDvRZcoAoYmbr4oSHD18brOXlk3WtyxGxFoEQPBZFvmf5XVDoGtCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79a4c8b6855156567008f86fe0cbd5d2793c7e0965992860fde361b2bd3d334e","last_reissued_at":"2026-05-18T00:15:43.737329Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:43.737329Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Single Shot Active Learning using Pseudo Annotators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"stat.ML","authors_text":"Marco Loog, Yazhou Yang","submitted_at":"2018-05-17T09:05:28Z","abstract_excerpt":"Standard myopic active learning assumes that human annotations are always obtainable whenever new samples are selected. This, however, is unrealistic in many real-world applications where human experts are not readily available at all times. In this paper, we consider the single shot setting: all the required samples should be chosen in a single shot and no human annotation can be exploited during the selection process. We propose a new method, Active Learning through Random Labeling (ALRL), which substitutes single human annotator for multiple, what we will refer to as, pseudo annotators. The"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.06660","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1805.06660","created_at":"2026-05-18T00:15:43.737401+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.06660v1","created_at":"2026-05-18T00:15:43.737401+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.06660","created_at":"2026-05-18T00:15:43.737401+00:00"},{"alias_kind":"pith_short_12","alias_value":"PGSMRNUFKFLF","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"PGSMRNUFKFLFM4AI","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"PGSMRNUF","created_at":"2026-05-18T12:32:43.782077+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PGSMRNUFKFLFM4AI7BX6BS6V2J","json":"https://pith.science/pith/PGSMRNUFKFLFM4AI7BX6BS6V2J.json","graph_json":"https://pith.science/api/pith-number/PGSMRNUFKFLFM4AI7BX6BS6V2J/graph.json","events_json":"https://pith.science/api/pith-number/PGSMRNUFKFLFM4AI7BX6BS6V2J/events.json","paper":"https://pith.science/paper/PGSMRNUF"},"agent_actions":{"view_html":"https://pith.science/pith/PGSMRNUFKFLFM4AI7BX6BS6V2J","download_json":"https://pith.science/pith/PGSMRNUFKFLFM4AI7BX6BS6V2J.json","view_paper":"https://pith.science/paper/PGSMRNUF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.06660&json=true","fetch_graph":"https://pith.science/api/pith-number/PGSMRNUFKFLFM4AI7BX6BS6V2J/graph.json","fetch_events":"https://pith.science/api/pith-number/PGSMRNUFKFLFM4AI7BX6BS6V2J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PGSMRNUFKFLFM4AI7BX6BS6V2J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PGSMRNUFKFLFM4AI7BX6BS6V2J/action/storage_attestation","attest_author":"https://pith.science/pith/PGSMRNUFKFLFM4AI7BX6BS6V2J/action/author_attestation","sign_citation":"https://pith.science/pith/PGSMRNUFKFLFM4AI7BX6BS6V2J/action/citation_signature","submit_replication":"https://pith.science/pith/PGSMRNUFKFLFM4AI7BX6BS6V2J/action/replication_record"}},"created_at":"2026-05-18T00:15:43.737401+00:00","updated_at":"2026-05-18T00:15:43.737401+00:00"}