{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:AIR5VAUOI63XGEEZW6K74BIFEN","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":"19212c361b8c67d6ab8e8dac9d0a19281580b0423540ff1f8da67afa0762cd53","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-08-18T13:49:10Z","title_canon_sha256":"b81b7c74e16df46dad8e6fb78f2d643336e24fddaa855e7a44a464fdbf423e65"},"schema_version":"1.0","source":{"id":"2208.08831","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.08831","created_at":"2026-07-05T06:09:05Z"},{"alias_kind":"arxiv_version","alias_value":"2208.08831v2","created_at":"2026-07-05T06:09:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.08831","created_at":"2026-07-05T06:09:05Z"},{"alias_kind":"pith_short_12","alias_value":"AIR5VAUOI63X","created_at":"2026-07-05T06:09:05Z"},{"alias_kind":"pith_short_16","alias_value":"AIR5VAUOI63XGEEZ","created_at":"2026-07-05T06:09:05Z"},{"alias_kind":"pith_short_8","alias_value":"AIR5VAUO","created_at":"2026-07-05T06:09:05Z"}],"graph_snapshots":[{"event_id":"sha256:4e6f6c3722f15fe547dcd69398cfcde1f3edb507529b4291bc20ad8d63e23237","target":"graph","created_at":"2026-07-05T06:09:05Z","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/2208.08831/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Automatically discovering failures in vision models under real-world settings remains an open challenge. This work demonstrates how off-the-shelf, large-scale, image-to-text and text-to-image models, trained on vast amounts of data, can be leveraged to automatically find such failures. In essence, a conditional text-to-image generative model is used to generate large amounts of synthetic, yet realistic, inputs given a ground-truth label. Misclassified inputs are clustered and a captioning model is used to describe each cluster. Each cluster's description is used in turn to generate more inputs","authors_text":"Isabela Albuquerque, Olivia Wiles, Sven Gowal","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-08-18T13:49:10Z","title":"Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.08831","kind":"arxiv","version":2},"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:f9a680f37d89b665c152e70c1a80375e66a7678332f6f0becdb811187f1e4b57","target":"record","created_at":"2026-07-05T06:09:05Z","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":"19212c361b8c67d6ab8e8dac9d0a19281580b0423540ff1f8da67afa0762cd53","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-08-18T13:49:10Z","title_canon_sha256":"b81b7c74e16df46dad8e6fb78f2d643336e24fddaa855e7a44a464fdbf423e65"},"schema_version":"1.0","source":{"id":"2208.08831","kind":"arxiv","version":2}},"canonical_sha256":"0223da828e47b7731099b795fe0505234c9591cefbad6ff633196d4d9bbd7654","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0223da828e47b7731099b795fe0505234c9591cefbad6ff633196d4d9bbd7654","first_computed_at":"2026-07-05T06:09:05.325643Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:09:05.325643Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ib4IYWXVN9zVNCcVeawK3CZGbF2ZJ1UtewdSwKcTi0PVwqcboNsuHO7DAiJqESiGLRiyiHDsUW5QjCUgoAZSCw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:09:05.326114Z","signed_message":"canonical_sha256_bytes"},"source_id":"2208.08831","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f9a680f37d89b665c152e70c1a80375e66a7678332f6f0becdb811187f1e4b57","sha256:4e6f6c3722f15fe547dcd69398cfcde1f3edb507529b4291bc20ad8d63e23237"],"state_sha256":"143b37eb026b3220fec71a9e1e2661bc236d8d576d9049f917b420e015010b2b"}