{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:OSVHVXPYYTOGEZPLAO56EWPQ5I","short_pith_number":"pith:OSVHVXPY","schema_version":"1.0","canonical_sha256":"74aa7addf8c4dc6265eb03bbe259f0ea08c4508c9a2542ccfb8c1a0f93a02ec2","source":{"kind":"arxiv","id":"2305.12954","version":1},"attestation_state":"computed","paper":{"title":"Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jian Yang, Penghai Zhao, Renjie Song, Xiang Li, Yuxuan Li, Zheng Li","submitted_at":"2023-05-22T12:02:31Z","abstract_excerpt":"Diffusion models have recently achieved astonishing performance in generating high-fidelity photo-realistic images. Given their huge success, it is still unclear whether synthetic images are applicable for knowledge distillation when real images are unavailable. In this paper, we extensively study whether and how synthetic images produced from state-of-the-art diffusion models can be used for knowledge distillation without access to real images, and obtain three key conclusions: (1) synthetic data from diffusion models can easily lead to state-of-the-art performance among existing synthesis-ba"},"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":"2305.12954","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-22T12:02:31Z","cross_cats_sorted":[],"title_canon_sha256":"71197c4b4d7801459418b145991f14c71e4558323fd49e0f5c00b282defaf918","abstract_canon_sha256":"d91902876204694ee7cb6d092c313241ca90533bd2c2af6cf46a7b7c7e3748b6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:12:24.975610Z","signature_b64":"M5tIUc1QbDZg+YcOh6pKED85VfIXUkNz4uNzKErICKQNNmSeD2gZKPNx2d+wp2gYfO3mm4igk3IIBYRz7MxKCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"74aa7addf8c4dc6265eb03bbe259f0ea08c4508c9a2542ccfb8c1a0f93a02ec2","last_reissued_at":"2026-07-05T06:12:24.975060Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:12:24.975060Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jian Yang, Penghai Zhao, Renjie Song, Xiang Li, Yuxuan Li, Zheng Li","submitted_at":"2023-05-22T12:02:31Z","abstract_excerpt":"Diffusion models have recently achieved astonishing performance in generating high-fidelity photo-realistic images. Given their huge success, it is still unclear whether synthetic images are applicable for knowledge distillation when real images are unavailable. In this paper, we extensively study whether and how synthetic images produced from state-of-the-art diffusion models can be used for knowledge distillation without access to real images, and obtain three key conclusions: (1) synthetic data from diffusion models can easily lead to state-of-the-art performance among existing synthesis-ba"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.12954","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2305.12954/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2305.12954","created_at":"2026-07-05T06:12:24.975118+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.12954v1","created_at":"2026-07-05T06:12:24.975118+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.12954","created_at":"2026-07-05T06:12:24.975118+00:00"},{"alias_kind":"pith_short_12","alias_value":"OSVHVXPYYTOG","created_at":"2026-07-05T06:12:24.975118+00:00"},{"alias_kind":"pith_short_16","alias_value":"OSVHVXPYYTOGEZPL","created_at":"2026-07-05T06:12:24.975118+00:00"},{"alias_kind":"pith_short_8","alias_value":"OSVHVXPY","created_at":"2026-07-05T06:12:24.975118+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.08116","citing_title":"Diffusion-based Data Augmentation and Knowledge Distillation with Generated Soft Labels Solving Data Scarcity Problems of SAR Oil Spill Segmentation","ref_index":34,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OSVHVXPYYTOGEZPLAO56EWPQ5I","json":"https://pith.science/pith/OSVHVXPYYTOGEZPLAO56EWPQ5I.json","graph_json":"https://pith.science/api/pith-number/OSVHVXPYYTOGEZPLAO56EWPQ5I/graph.json","events_json":"https://pith.science/api/pith-number/OSVHVXPYYTOGEZPLAO56EWPQ5I/events.json","paper":"https://pith.science/paper/OSVHVXPY"},"agent_actions":{"view_html":"https://pith.science/pith/OSVHVXPYYTOGEZPLAO56EWPQ5I","download_json":"https://pith.science/pith/OSVHVXPYYTOGEZPLAO56EWPQ5I.json","view_paper":"https://pith.science/paper/OSVHVXPY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.12954&json=true","fetch_graph":"https://pith.science/api/pith-number/OSVHVXPYYTOGEZPLAO56EWPQ5I/graph.json","fetch_events":"https://pith.science/api/pith-number/OSVHVXPYYTOGEZPLAO56EWPQ5I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OSVHVXPYYTOGEZPLAO56EWPQ5I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OSVHVXPYYTOGEZPLAO56EWPQ5I/action/storage_attestation","attest_author":"https://pith.science/pith/OSVHVXPYYTOGEZPLAO56EWPQ5I/action/author_attestation","sign_citation":"https://pith.science/pith/OSVHVXPYYTOGEZPLAO56EWPQ5I/action/citation_signature","submit_replication":"https://pith.science/pith/OSVHVXPYYTOGEZPLAO56EWPQ5I/action/replication_record"}},"created_at":"2026-07-05T06:12:24.975118+00:00","updated_at":"2026-07-05T06:12:24.975118+00:00"}