{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:UTOXYSFG2QNELV6HQYZZEQELBA","short_pith_number":"pith:UTOXYSFG","schema_version":"1.0","canonical_sha256":"a4dd7c48a6d41a45d7c7863392408b0809f1301ea8eca8c056621195affecaa1","source":{"kind":"arxiv","id":"1606.04586","version":1},"attestation_state":"computed","paper":{"title":"Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mehdi Sajjadi, Mehran Javanmardi, Tolga Tasdizen","submitted_at":"2016-06-14T22:30:08Z","abstract_excerpt":"Effective convolutional neural networks are trained on large sets of labeled data. However, creating large labeled datasets is a very costly and time-consuming task. Semi-supervised learning uses unlabeled data to train a model with higher accuracy when there is a limited set of labeled data available. In this paper, we consider the problem of semi-supervised learning with convolutional neural networks. Techniques such as randomized data augmentation, dropout and random max-pooling provide better generalization and stability for classifiers that are trained using gradient descent. Multiple pas"},"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":"1606.04586","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-14T22:30:08Z","cross_cats_sorted":[],"title_canon_sha256":"586ac2b8e8627574cbc039a952b7cdaf52d80d8c617d84710d8426f49854aa13","abstract_canon_sha256":"12a98de58839ffdd649124c58a81a98d2f23a3ec493a097658629396bf083ce1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:12:24.748968Z","signature_b64":"2M4/HjNQCWvsmydzm2mNUU+hoGIz44i9oWXGRGZu6D84it33HEs6J1ecp0YOlS+V9oglTQf3EfXfBNgmFFc7Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a4dd7c48a6d41a45d7c7863392408b0809f1301ea8eca8c056621195affecaa1","last_reissued_at":"2026-05-18T01:12:24.748636Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:12:24.748636Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mehdi Sajjadi, Mehran Javanmardi, Tolga Tasdizen","submitted_at":"2016-06-14T22:30:08Z","abstract_excerpt":"Effective convolutional neural networks are trained on large sets of labeled data. However, creating large labeled datasets is a very costly and time-consuming task. Semi-supervised learning uses unlabeled data to train a model with higher accuracy when there is a limited set of labeled data available. In this paper, we consider the problem of semi-supervised learning with convolutional neural networks. Techniques such as randomized data augmentation, dropout and random max-pooling provide better generalization and stability for classifiers that are trained using gradient descent. Multiple pas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.04586","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":"1606.04586","created_at":"2026-05-18T01:12:24.748689+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.04586v1","created_at":"2026-05-18T01:12:24.748689+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.04586","created_at":"2026-05-18T01:12:24.748689+00:00"},{"alias_kind":"pith_short_12","alias_value":"UTOXYSFG2QNE","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_16","alias_value":"UTOXYSFG2QNELV6H","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_8","alias_value":"UTOXYSFG","created_at":"2026-05-18T12:30:46.583412+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/UTOXYSFG2QNELV6HQYZZEQELBA","json":"https://pith.science/pith/UTOXYSFG2QNELV6HQYZZEQELBA.json","graph_json":"https://pith.science/api/pith-number/UTOXYSFG2QNELV6HQYZZEQELBA/graph.json","events_json":"https://pith.science/api/pith-number/UTOXYSFG2QNELV6HQYZZEQELBA/events.json","paper":"https://pith.science/paper/UTOXYSFG"},"agent_actions":{"view_html":"https://pith.science/pith/UTOXYSFG2QNELV6HQYZZEQELBA","download_json":"https://pith.science/pith/UTOXYSFG2QNELV6HQYZZEQELBA.json","view_paper":"https://pith.science/paper/UTOXYSFG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.04586&json=true","fetch_graph":"https://pith.science/api/pith-number/UTOXYSFG2QNELV6HQYZZEQELBA/graph.json","fetch_events":"https://pith.science/api/pith-number/UTOXYSFG2QNELV6HQYZZEQELBA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UTOXYSFG2QNELV6HQYZZEQELBA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UTOXYSFG2QNELV6HQYZZEQELBA/action/storage_attestation","attest_author":"https://pith.science/pith/UTOXYSFG2QNELV6HQYZZEQELBA/action/author_attestation","sign_citation":"https://pith.science/pith/UTOXYSFG2QNELV6HQYZZEQELBA/action/citation_signature","submit_replication":"https://pith.science/pith/UTOXYSFG2QNELV6HQYZZEQELBA/action/replication_record"}},"created_at":"2026-05-18T01:12:24.748689+00:00","updated_at":"2026-05-18T01:12:24.748689+00:00"}