{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:SC3XQX5P4EYLBPQJUT762ZJY7Y","short_pith_number":"pith:SC3XQX5P","schema_version":"1.0","canonical_sha256":"90b7785fafe130b0be09a4ffed6538fe24c3791d43d99bb736e360e78ad988e6","source":{"kind":"arxiv","id":"1708.00489","version":4},"attestation_state":"computed","paper":{"title":"Active Learning for Convolutional Neural Networks: A Core-Set Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"stat.ML","authors_text":"Ozan Sener, Silvio Savarese","submitted_at":"2017-08-01T19:50:53Z","abstract_excerpt":"Convolutional neural networks (CNNs) have been successfully applied to many recognition and learning tasks using a universal recipe; training a deep model on a very large dataset of supervised examples. However, this approach is rather restrictive in practice since collecting a large set of labeled images is very expensive. One way to ease this problem is coming up with smart ways for choosing images to be labelled from a very large collection (ie. active learning).\n  Our empirical study suggests that many of the active learning heuristics in the literature are not effective when applied to CN"},"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":"1708.00489","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-08-01T19:50:53Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"344b5f0980cf03bd524d7d86c180be36edde03a1414088e14694c7361408d127","abstract_canon_sha256":"20dde01bab6a0970104c77a1de5bea0192d1488ee76e550bb7e0326032ac6264"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:28.848544Z","signature_b64":"pOYg1ARG7BUaWOJjuKFKx3HNLN1iFc/qs2OZuy46W6CZYuStg/hx8WA5md+DadMAkejBodfAj8azP5rHBeUIBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90b7785fafe130b0be09a4ffed6538fe24c3791d43d99bb736e360e78ad988e6","last_reissued_at":"2026-05-18T00:14:28.847807Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:28.847807Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Active Learning for Convolutional Neural Networks: A Core-Set Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"stat.ML","authors_text":"Ozan Sener, Silvio Savarese","submitted_at":"2017-08-01T19:50:53Z","abstract_excerpt":"Convolutional neural networks (CNNs) have been successfully applied to many recognition and learning tasks using a universal recipe; training a deep model on a very large dataset of supervised examples. However, this approach is rather restrictive in practice since collecting a large set of labeled images is very expensive. One way to ease this problem is coming up with smart ways for choosing images to be labelled from a very large collection (ie. active learning).\n  Our empirical study suggests that many of the active learning heuristics in the literature are not effective when applied to CN"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.00489","kind":"arxiv","version":4},"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":"1708.00489","created_at":"2026-05-18T00:14:28.847927+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.00489v4","created_at":"2026-05-18T00:14:28.847927+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.00489","created_at":"2026-05-18T00:14:28.847927+00:00"},{"alias_kind":"pith_short_12","alias_value":"SC3XQX5P4EYL","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_16","alias_value":"SC3XQX5P4EYLBPQJ","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_8","alias_value":"SC3XQX5P","created_at":"2026-05-18T12:31:43.269735+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":33,"internal_anchor_count":15,"sample":[{"citing_arxiv_id":"2605.23482","citing_title":"Multimodal Distribution Matching for Vision-Language Dataset Distillation","ref_index":56,"is_internal_anchor":true},{"citing_arxiv_id":"2605.18762","citing_title":"ALDEN: Boosting Private Data Extraction from Retrieval-Augmented Generation Systems via Active Learning and Distribution Estimation","ref_index":58,"is_internal_anchor":true},{"citing_arxiv_id":"2605.10075","citing_title":"Active Testing of Large Language Models via Approximate Neyman Allocation","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2407.21772","citing_title":"ShieldGemma: Generative AI Content Moderation Based on Gemma","ref_index":19,"is_internal_anchor":true},{"citing_arxiv_id":"2605.19890","citing_title":"GoTTA be Diverse: Rethinking Memory Policies for Test-Time Adaptation","ref_index":32,"is_internal_anchor":true},{"citing_arxiv_id":"2605.02609","citing_title":"Gradient-Discrepancy Acquisition for Pool-Based Active Learning","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"2506.01942","citing_title":"OD3: Optimization-free Dataset Distillation for Object Detection","ref_index":20,"is_internal_anchor":true},{"citing_arxiv_id":"2502.10248","citing_title":"Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model","ref_index":281,"is_internal_anchor":true},{"citing_arxiv_id":"2507.09001","citing_title":"Surprisingly High Redundancy in Electronic Structure Data Across Materials Explained by Low Intrinsic Dimensionality","ref_index":72,"is_internal_anchor":true},{"citing_arxiv_id":"2510.03247","citing_title":"Towards Multimodal Active Learning: Efficient Learning with Limited Paired Data","ref_index":8,"is_internal_anchor":true},{"citing_arxiv_id":"2604.13041","citing_title":"TableNet A Large-Scale Table Dataset with LLM-Powered Autonomous","ref_index":5,"is_internal_anchor":true},{"citing_arxiv_id":"2605.13942","citing_title":"EMA: Efficient Model Adaptation for Learning-based Systems","ref_index":28,"is_internal_anchor":true},{"citing_arxiv_id":"2605.14689","citing_title":"Are Candidate Models Really Needed for Active Learning?","ref_index":90,"is_internal_anchor":true},{"citing_arxiv_id":"2603.24480","citing_title":"Positive-First Most Ambiguous: A Simple Active Learning Criterion for Interactive Retrieval of Rare Categories","ref_index":35,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11231","citing_title":"LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection","ref_index":41,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11231","citing_title":"LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09935","citing_title":"Evidence-based Decision Modeling for Synthetic Face Detection with Uncertainty-driven Active Learning","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10075","citing_title":"Active Testing of Large Language Models via Approximate Neyman Allocation","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10784","citing_title":"MASS-DPO: Multi-negative Active Sample Selection for Direct Policy Optimization","ref_index":53,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09858","citing_title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10349","citing_title":"Portable Active Learning for Object Detection","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05590","citing_title":"Uncertainty-Guided Edge Learning for Deep Image Regression in Remote Sensing","ref_index":44,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19027","citing_title":"Neural Operator Representation of Granular Micromechanics-based Failure Envelope","ref_index":80,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19335","citing_title":"When Active Learning Falls Short: An Empirical Study on Chemical Reaction Extraction","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2604.11328","citing_title":"Select Smarter, Not More: Prompt-Aware Evaluation Scheduling with Submodular Guarantees","ref_index":36,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SC3XQX5P4EYLBPQJUT762ZJY7Y","json":"https://pith.science/pith/SC3XQX5P4EYLBPQJUT762ZJY7Y.json","graph_json":"https://pith.science/api/pith-number/SC3XQX5P4EYLBPQJUT762ZJY7Y/graph.json","events_json":"https://pith.science/api/pith-number/SC3XQX5P4EYLBPQJUT762ZJY7Y/events.json","paper":"https://pith.science/paper/SC3XQX5P"},"agent_actions":{"view_html":"https://pith.science/pith/SC3XQX5P4EYLBPQJUT762ZJY7Y","download_json":"https://pith.science/pith/SC3XQX5P4EYLBPQJUT762ZJY7Y.json","view_paper":"https://pith.science/paper/SC3XQX5P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.00489&json=true","fetch_graph":"https://pith.science/api/pith-number/SC3XQX5P4EYLBPQJUT762ZJY7Y/graph.json","fetch_events":"https://pith.science/api/pith-number/SC3XQX5P4EYLBPQJUT762ZJY7Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SC3XQX5P4EYLBPQJUT762ZJY7Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SC3XQX5P4EYLBPQJUT762ZJY7Y/action/storage_attestation","attest_author":"https://pith.science/pith/SC3XQX5P4EYLBPQJUT762ZJY7Y/action/author_attestation","sign_citation":"https://pith.science/pith/SC3XQX5P4EYLBPQJUT762ZJY7Y/action/citation_signature","submit_replication":"https://pith.science/pith/SC3XQX5P4EYLBPQJUT762ZJY7Y/action/replication_record"}},"created_at":"2026-05-18T00:14:28.847927+00:00","updated_at":"2026-05-18T00:14:28.847927+00:00"}