{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:IJEOZJJCXULLJ24YEG2LAYGFNV","short_pith_number":"pith:IJEOZJJC","canonical_record":{"source":{"id":"2106.05527","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-10T06:30:16Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"e67599c3c69c234d537175632ec9b8e6cb7e66e4a238f55df67209bf19d7eec9","abstract_canon_sha256":"56fe992e02d22002eeb1d93f0604ef2bca7eb0fe6bb333fdb66d6f7bb34df190"},"schema_version":"1.0"},"canonical_sha256":"4248eca522bd16b4eb9821b4b060c56d6d825a3c788330c89f7ce91e0c296d14","source":{"kind":"arxiv","id":"2106.05527","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.05527","created_at":"2026-07-05T04:30:51Z"},{"alias_kind":"arxiv_version","alias_value":"2106.05527v5","created_at":"2026-07-05T04:30:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.05527","created_at":"2026-07-05T04:30:51Z"},{"alias_kind":"pith_short_12","alias_value":"IJEOZJJCXULL","created_at":"2026-07-05T04:30:51Z"},{"alias_kind":"pith_short_16","alias_value":"IJEOZJJCXULLJ24Y","created_at":"2026-07-05T04:30:51Z"},{"alias_kind":"pith_short_8","alias_value":"IJEOZJJC","created_at":"2026-07-05T04:30:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:IJEOZJJCXULLJ24YEG2LAYGFNV","target":"record","payload":{"canonical_record":{"source":{"id":"2106.05527","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-10T06:30:16Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"e67599c3c69c234d537175632ec9b8e6cb7e66e4a238f55df67209bf19d7eec9","abstract_canon_sha256":"56fe992e02d22002eeb1d93f0604ef2bca7eb0fe6bb333fdb66d6f7bb34df190"},"schema_version":"1.0"},"canonical_sha256":"4248eca522bd16b4eb9821b4b060c56d6d825a3c788330c89f7ce91e0c296d14","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:30:51.409157Z","signature_b64":"yH5cWmiO/x1gDqmh9THHfc7Xy7MIddOCF2U4wNSs1CafA8XMKqCsYMxcw2NUcf2NtJGwO2n1CNN0dbkwTmq9Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4248eca522bd16b4eb9821b4b060c56d6d825a3c788330c89f7ce91e0c296d14","last_reissued_at":"2026-07-05T04:30:51.408784Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:30:51.408784Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2106.05527","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:30:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/rkoTHG3ba/pl9rLruwR1WBAJcjj52mbosUn/6Gwr92/XsB5kWKndBq/W+1EPpb04aZylcYdxTL3nZwUrl1QBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T08:07:25.576418Z"},"content_sha256":"11ceb24eefc71c6fdd4d27e9f897a91fd29d4ec082e8fe33a00d00edbd2fb54e","schema_version":"1.0","event_id":"sha256:11ceb24eefc71c6fdd4d27e9f897a91fd29d4ec082e8fe33a00d00edbd2fb54e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:IJEOZJJCXULLJ24YEG2LAYGFNV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Dongjun Kim, Il-Chul Moon, Kyungwoo Song, Seungjae Shin, Wanmo Kang","submitted_at":"2021-06-10T06:30:16Z","abstract_excerpt":"Recent advances in diffusion models bring state-of-the-art performance on image generation tasks. However, empirical results from previous research in diffusion models imply an inverse correlation between density estimation and sample generation performances. This paper investigates with sufficient empirical evidence that such inverse correlation happens because density estimation is significantly contributed by small diffusion time, whereas sample generation mainly depends on large diffusion time. However, training a score network well across the entire diffusion time is demanding because the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.05527","kind":"arxiv","version":5},"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/2106.05527/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:30:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gtylPVXVJGWd1AJIG0XlA9ul7Ph8ZAwMAh+VtFpXmfaL4hBje/023hj2yjQERAs5H++IA7q6PNlRlttkBxaYDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T08:07:25.576783Z"},"content_sha256":"deaf87c2797d21a0d4b02f7b4facba82ccdb628d9cd3c57fe517c638bcb309d8","schema_version":"1.0","event_id":"sha256:deaf87c2797d21a0d4b02f7b4facba82ccdb628d9cd3c57fe517c638bcb309d8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IJEOZJJCXULLJ24YEG2LAYGFNV/bundle.json","state_url":"https://pith.science/pith/IJEOZJJCXULLJ24YEG2LAYGFNV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IJEOZJJCXULLJ24YEG2LAYGFNV/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":"2026-07-07T08:07:25Z","links":{"resolver":"https://pith.science/pith/IJEOZJJCXULLJ24YEG2LAYGFNV","bundle":"https://pith.science/pith/IJEOZJJCXULLJ24YEG2LAYGFNV/bundle.json","state":"https://pith.science/pith/IJEOZJJCXULLJ24YEG2LAYGFNV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IJEOZJJCXULLJ24YEG2LAYGFNV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:IJEOZJJCXULLJ24YEG2LAYGFNV","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":"56fe992e02d22002eeb1d93f0604ef2bca7eb0fe6bb333fdb66d6f7bb34df190","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-10T06:30:16Z","title_canon_sha256":"e67599c3c69c234d537175632ec9b8e6cb7e66e4a238f55df67209bf19d7eec9"},"schema_version":"1.0","source":{"id":"2106.05527","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.05527","created_at":"2026-07-05T04:30:51Z"},{"alias_kind":"arxiv_version","alias_value":"2106.05527v5","created_at":"2026-07-05T04:30:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.05527","created_at":"2026-07-05T04:30:51Z"},{"alias_kind":"pith_short_12","alias_value":"IJEOZJJCXULL","created_at":"2026-07-05T04:30:51Z"},{"alias_kind":"pith_short_16","alias_value":"IJEOZJJCXULLJ24Y","created_at":"2026-07-05T04:30:51Z"},{"alias_kind":"pith_short_8","alias_value":"IJEOZJJC","created_at":"2026-07-05T04:30:51Z"}],"graph_snapshots":[{"event_id":"sha256:deaf87c2797d21a0d4b02f7b4facba82ccdb628d9cd3c57fe517c638bcb309d8","target":"graph","created_at":"2026-07-05T04:30:51Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2106.05527/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in diffusion models bring state-of-the-art performance on image generation tasks. However, empirical results from previous research in diffusion models imply an inverse correlation between density estimation and sample generation performances. This paper investigates with sufficient empirical evidence that such inverse correlation happens because density estimation is significantly contributed by small diffusion time, whereas sample generation mainly depends on large diffusion time. However, training a score network well across the entire diffusion time is demanding because the","authors_text":"Dongjun Kim, Il-Chul Moon, Kyungwoo Song, Seungjae Shin, Wanmo Kang","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-10T06:30:16Z","title":"Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.05527","kind":"arxiv","version":5},"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":"sha256:11ceb24eefc71c6fdd4d27e9f897a91fd29d4ec082e8fe33a00d00edbd2fb54e","target":"record","created_at":"2026-07-05T04:30:51Z","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":"56fe992e02d22002eeb1d93f0604ef2bca7eb0fe6bb333fdb66d6f7bb34df190","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-10T06:30:16Z","title_canon_sha256":"e67599c3c69c234d537175632ec9b8e6cb7e66e4a238f55df67209bf19d7eec9"},"schema_version":"1.0","source":{"id":"2106.05527","kind":"arxiv","version":5}},"canonical_sha256":"4248eca522bd16b4eb9821b4b060c56d6d825a3c788330c89f7ce91e0c296d14","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4248eca522bd16b4eb9821b4b060c56d6d825a3c788330c89f7ce91e0c296d14","first_computed_at":"2026-07-05T04:30:51.408784Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:30:51.408784Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yH5cWmiO/x1gDqmh9THHfc7Xy7MIddOCF2U4wNSs1CafA8XMKqCsYMxcw2NUcf2NtJGwO2n1CNN0dbkwTmq9Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:30:51.409157Z","signed_message":"canonical_sha256_bytes"},"source_id":"2106.05527","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:11ceb24eefc71c6fdd4d27e9f897a91fd29d4ec082e8fe33a00d00edbd2fb54e","sha256:deaf87c2797d21a0d4b02f7b4facba82ccdb628d9cd3c57fe517c638bcb309d8"],"state_sha256":"bb6283130b8016ffeb127ef6999ac1261eb08da3b10cf7b3430457c8e86a044a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d49NRAwjprFFMyWd+D5KRd8KXPzXQ7WRqYHKveRxVRzLAsYKL/rFAeyQftxWJMO+VH5Sa/Q6j335xd3jYy8PCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T08:07:25.578745Z","bundle_sha256":"976f7a80303b518e7e17bd815eba7732c06d82a4fa5abf6a1fb2b87a17b669d4"}}