{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:ONYU7ENG2OVO64GE7KRUKGAUXW","short_pith_number":"pith:ONYU7ENG","canonical_record":{"source":{"id":"2409.16949","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-25T14:02:43Z","cross_cats_sorted":[],"title_canon_sha256":"7c6c95ce0658ea357ec6270a64dd671c0bf70845f5ec02d2c5f56798a87648bc","abstract_canon_sha256":"a0c7b2fe95fa4595c09ac519a24f9696c7115c1a159ead3067c00fa33adc9ce2"},"schema_version":"1.0"},"canonical_sha256":"73714f91a6d3aaef70c4faa3451814bdbc0e264ca5015aaad2a93bb75a7cdd88","source":{"kind":"arxiv","id":"2409.16949","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.16949","created_at":"2026-07-05T09:11:41Z"},{"alias_kind":"arxiv_version","alias_value":"2409.16949v1","created_at":"2026-07-05T09:11:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.16949","created_at":"2026-07-05T09:11:41Z"},{"alias_kind":"pith_short_12","alias_value":"ONYU7ENG2OVO","created_at":"2026-07-05T09:11:41Z"},{"alias_kind":"pith_short_16","alias_value":"ONYU7ENG2OVO64GE","created_at":"2026-07-05T09:11:41Z"},{"alias_kind":"pith_short_8","alias_value":"ONYU7ENG","created_at":"2026-07-05T09:11:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:ONYU7ENG2OVO64GE7KRUKGAUXW","target":"record","payload":{"canonical_record":{"source":{"id":"2409.16949","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-25T14:02:43Z","cross_cats_sorted":[],"title_canon_sha256":"7c6c95ce0658ea357ec6270a64dd671c0bf70845f5ec02d2c5f56798a87648bc","abstract_canon_sha256":"a0c7b2fe95fa4595c09ac519a24f9696c7115c1a159ead3067c00fa33adc9ce2"},"schema_version":"1.0"},"canonical_sha256":"73714f91a6d3aaef70c4faa3451814bdbc0e264ca5015aaad2a93bb75a7cdd88","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:11:41.958201Z","signature_b64":"FCgAUo96kMXvvoI3j5crFGog3asmXWtK52fQqDvdBoinY61PIoXbFGH5Nn4cuLpY/9nsZi5dHaPfYnZLn5JGAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73714f91a6d3aaef70c4faa3451814bdbc0e264ca5015aaad2a93bb75a7cdd88","last_reissued_at":"2026-07-05T09:11:41.957840Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:11:41.957840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.16949","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:11:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xmf01IhB+yCf0aCP1adhQ7Y9AUr4gMaM3b6+FZkkxlqU7OIBL8UUXmq8JRsvDNC94np5vYRVKWihOQR1pn+JAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-11T22:09:23.488291Z"},"content_sha256":"ad949f8a7b0e8e80e8591e451c4c7ee377ec9d3951ed60f0ae17c26a2daed7ad","schema_version":"1.0","event_id":"sha256:ad949f8a7b0e8e80e8591e451c4c7ee377ec9d3951ed60f0ae17c26a2daed7ad"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:ONYU7ENG2OVO64GE7KRUKGAUXW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance Scaling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hyun-seok Min, Jaeyoung Kim, Kyuheon Jung, Seongwoo Cho, Sungchul Choi, Yongdeuk Seo","submitted_at":"2024-09-25T14:02:43Z","abstract_excerpt":"In this paper, we present an effective data augmentation framework leveraging the Large Language Model (LLM) and Diffusion Model (DM) to tackle the challenges inherent in data-scarce scenarios. Recently, DMs have opened up the possibility of generating synthetic images to complement a few training images. However, increasing the diversity of synthetic images also raises the risk of generating samples outside the target distribution. Our approach addresses this issue by embedding novel semantic information into text prompts via LLM and utilizing real images as visual prompts, thus generating se"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.16949","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/2409.16949/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":"2026-07-05T09:11:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fBQ9gn86HFXVE+90LqDNPRG16CBIYLOSZVdtmKmtqHr/pMU4MvlsftkmMP0VOJanzcpS7k7dazoSXiwSaeTTDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-11T22:09:23.488922Z"},"content_sha256":"e8eb86be45c978ac6a1765dbee779831d8f414dd35c40c56500f385536a6f418","schema_version":"1.0","event_id":"sha256:e8eb86be45c978ac6a1765dbee779831d8f414dd35c40c56500f385536a6f418"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ONYU7ENG2OVO64GE7KRUKGAUXW/bundle.json","state_url":"https://pith.science/pith/ONYU7ENG2OVO64GE7KRUKGAUXW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ONYU7ENG2OVO64GE7KRUKGAUXW/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":"2026-07-11T22:09:23Z","links":{"resolver":"https://pith.science/pith/ONYU7ENG2OVO64GE7KRUKGAUXW","bundle":"https://pith.science/pith/ONYU7ENG2OVO64GE7KRUKGAUXW/bundle.json","state":"https://pith.science/pith/ONYU7ENG2OVO64GE7KRUKGAUXW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ONYU7ENG2OVO64GE7KRUKGAUXW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ONYU7ENG2OVO64GE7KRUKGAUXW","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":"a0c7b2fe95fa4595c09ac519a24f9696c7115c1a159ead3067c00fa33adc9ce2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-25T14:02:43Z","title_canon_sha256":"7c6c95ce0658ea357ec6270a64dd671c0bf70845f5ec02d2c5f56798a87648bc"},"schema_version":"1.0","source":{"id":"2409.16949","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.16949","created_at":"2026-07-05T09:11:41Z"},{"alias_kind":"arxiv_version","alias_value":"2409.16949v1","created_at":"2026-07-05T09:11:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.16949","created_at":"2026-07-05T09:11:41Z"},{"alias_kind":"pith_short_12","alias_value":"ONYU7ENG2OVO","created_at":"2026-07-05T09:11:41Z"},{"alias_kind":"pith_short_16","alias_value":"ONYU7ENG2OVO64GE","created_at":"2026-07-05T09:11:41Z"},{"alias_kind":"pith_short_8","alias_value":"ONYU7ENG","created_at":"2026-07-05T09:11:41Z"}],"graph_snapshots":[{"event_id":"sha256:e8eb86be45c978ac6a1765dbee779831d8f414dd35c40c56500f385536a6f418","target":"graph","created_at":"2026-07-05T09:11:41Z","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/2409.16949/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we present an effective data augmentation framework leveraging the Large Language Model (LLM) and Diffusion Model (DM) to tackle the challenges inherent in data-scarce scenarios. Recently, DMs have opened up the possibility of generating synthetic images to complement a few training images. However, increasing the diversity of synthetic images also raises the risk of generating samples outside the target distribution. Our approach addresses this issue by embedding novel semantic information into text prompts via LLM and utilizing real images as visual prompts, thus generating se","authors_text":"Hyun-seok Min, Jaeyoung Kim, Kyuheon Jung, Seongwoo Cho, Sungchul Choi, Yongdeuk Seo","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-25T14:02:43Z","title":"DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance Scaling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.16949","kind":"arxiv","version":1},"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:ad949f8a7b0e8e80e8591e451c4c7ee377ec9d3951ed60f0ae17c26a2daed7ad","target":"record","created_at":"2026-07-05T09:11:41Z","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":"a0c7b2fe95fa4595c09ac519a24f9696c7115c1a159ead3067c00fa33adc9ce2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-25T14:02:43Z","title_canon_sha256":"7c6c95ce0658ea357ec6270a64dd671c0bf70845f5ec02d2c5f56798a87648bc"},"schema_version":"1.0","source":{"id":"2409.16949","kind":"arxiv","version":1}},"canonical_sha256":"73714f91a6d3aaef70c4faa3451814bdbc0e264ca5015aaad2a93bb75a7cdd88","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"73714f91a6d3aaef70c4faa3451814bdbc0e264ca5015aaad2a93bb75a7cdd88","first_computed_at":"2026-07-05T09:11:41.957840Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:11:41.957840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FCgAUo96kMXvvoI3j5crFGog3asmXWtK52fQqDvdBoinY61PIoXbFGH5Nn4cuLpY/9nsZi5dHaPfYnZLn5JGAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:11:41.958201Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.16949","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ad949f8a7b0e8e80e8591e451c4c7ee377ec9d3951ed60f0ae17c26a2daed7ad","sha256:e8eb86be45c978ac6a1765dbee779831d8f414dd35c40c56500f385536a6f418"],"state_sha256":"8d5efa2461ce7e2c725fb91694b2f0f3f37309b4ba83591b0deb85d94a24a1b3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tHbW5CirlsCpRzE558fT70XWDypgFr1ih5LfGO6kqbRhBVTtUBNXGUzIWlEzUwij2wlO8NfEEC/5rtQq+3xKBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-11T22:09:23.492298Z","bundle_sha256":"caeaea9b99eb2056b9618fbd301881002e0c6e111f03b52830625e933a8e1813"}}