{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:IWQ6DEAU6IELDHB7K4AH6YSDRR","short_pith_number":"pith:IWQ6DEAU","canonical_record":{"source":{"id":"1512.01320","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-12-04T05:48:09Z","cross_cats_sorted":[],"title_canon_sha256":"0dc4e7a6771213e38740fdb773a3b1427d21c362d500c22c35d296652ce3e73a","abstract_canon_sha256":"e9eb5e08f0a95a892cd05d7efc9a016708e3f9174a2a67494c0c15cf8f8d4dc7"},"schema_version":"1.0"},"canonical_sha256":"45a1e19014f208b19c3f57007f62438c50a9279b58cecfeb764322e5b5926d6e","source":{"kind":"arxiv","id":"1512.01320","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1512.01320","created_at":"2026-05-18T01:22:00Z"},{"alias_kind":"arxiv_version","alias_value":"1512.01320v2","created_at":"2026-05-18T01:22:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.01320","created_at":"2026-05-18T01:22:00Z"},{"alias_kind":"pith_short_12","alias_value":"IWQ6DEAU6IEL","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_16","alias_value":"IWQ6DEAU6IELDHB7","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_8","alias_value":"IWQ6DEAU","created_at":"2026-05-18T12:29:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:IWQ6DEAU6IELDHB7K4AH6YSDRR","target":"record","payload":{"canonical_record":{"source":{"id":"1512.01320","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-12-04T05:48:09Z","cross_cats_sorted":[],"title_canon_sha256":"0dc4e7a6771213e38740fdb773a3b1427d21c362d500c22c35d296652ce3e73a","abstract_canon_sha256":"e9eb5e08f0a95a892cd05d7efc9a016708e3f9174a2a67494c0c15cf8f8d4dc7"},"schema_version":"1.0"},"canonical_sha256":"45a1e19014f208b19c3f57007f62438c50a9279b58cecfeb764322e5b5926d6e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:22:00.197440Z","signature_b64":"QLBAa1xmq6GKNUc3zsMY12w8GX7/UOOV9UPiQSnMFuZLQAlmpiNDdbvwBafxKwMnKZXB7riCmazFYwHzN3kJCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45a1e19014f208b19c3f57007f62438c50a9279b58cecfeb764322e5b5926d6e","last_reissued_at":"2026-05-18T01:22:00.197065Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:22:00.197065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1512.01320","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:22:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aB389553mxODu+lAyb802cDlXtexoCh4EYJVSlQYfL5KpgfHTLfyDI2kTNnsTGju8kk79Oz+Z7F7nwZz7WZQDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T11:59:24.017915Z"},"content_sha256":"4ea79368c6f97b082d95a5b179f677e72e530d07ac53bf5c9d8be67588a9386d","schema_version":"1.0","event_id":"sha256:4ea79368c6f97b082d95a5b179f677e72e530d07ac53bf5c9d8be67588a9386d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:IWQ6DEAU6IELDHB7K4AH6YSDRR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"What can we learn about CNNs from a large scale controlled object dataset?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ali Borji, Laurent Itti, Saeed Izadi","submitted_at":"2015-12-04T05:48:09Z","abstract_excerpt":"Tolerance to image variations (e.g. translation, scale, pose, illumination) is an important desired property of any object recognition system, be it human or machine. Moving towards increasingly bigger datasets has been trending in computer vision specially with the emergence of highly popular deep learning models. While being very useful for learning invariance to object inter- and intra-class shape variability, these large-scale wild datasets are not very useful for learning invariance to other parameters forcing researchers to resort to other tricks for training a model. In this work, we in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.01320","kind":"arxiv","version":2},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:22:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FlkzuuLRbA/Q60U0bL3F+nyq3LvN90XccGFuSrftcFANxV6vY/ui884uSK7zRbRXj2XNdoXWZ1wNcuSpa8YxBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T11:59:24.018622Z"},"content_sha256":"e6290ce91fe29edd062dba8a533120a934a7cfe2eb2dfa35da1f0afdda612fc9","schema_version":"1.0","event_id":"sha256:e6290ce91fe29edd062dba8a533120a934a7cfe2eb2dfa35da1f0afdda612fc9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IWQ6DEAU6IELDHB7K4AH6YSDRR/bundle.json","state_url":"https://pith.science/pith/IWQ6DEAU6IELDHB7K4AH6YSDRR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IWQ6DEAU6IELDHB7K4AH6YSDRR/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":"2026-06-06T11:59:24Z","links":{"resolver":"https://pith.science/pith/IWQ6DEAU6IELDHB7K4AH6YSDRR","bundle":"https://pith.science/pith/IWQ6DEAU6IELDHB7K4AH6YSDRR/bundle.json","state":"https://pith.science/pith/IWQ6DEAU6IELDHB7K4AH6YSDRR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IWQ6DEAU6IELDHB7K4AH6YSDRR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:IWQ6DEAU6IELDHB7K4AH6YSDRR","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":"e9eb5e08f0a95a892cd05d7efc9a016708e3f9174a2a67494c0c15cf8f8d4dc7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-12-04T05:48:09Z","title_canon_sha256":"0dc4e7a6771213e38740fdb773a3b1427d21c362d500c22c35d296652ce3e73a"},"schema_version":"1.0","source":{"id":"1512.01320","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1512.01320","created_at":"2026-05-18T01:22:00Z"},{"alias_kind":"arxiv_version","alias_value":"1512.01320v2","created_at":"2026-05-18T01:22:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.01320","created_at":"2026-05-18T01:22:00Z"},{"alias_kind":"pith_short_12","alias_value":"IWQ6DEAU6IEL","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_16","alias_value":"IWQ6DEAU6IELDHB7","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_8","alias_value":"IWQ6DEAU","created_at":"2026-05-18T12:29:27Z"}],"graph_snapshots":[{"event_id":"sha256:e6290ce91fe29edd062dba8a533120a934a7cfe2eb2dfa35da1f0afdda612fc9","target":"graph","created_at":"2026-05-18T01:22:00Z","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"},"paper":{"abstract_excerpt":"Tolerance to image variations (e.g. translation, scale, pose, illumination) is an important desired property of any object recognition system, be it human or machine. Moving towards increasingly bigger datasets has been trending in computer vision specially with the emergence of highly popular deep learning models. While being very useful for learning invariance to object inter- and intra-class shape variability, these large-scale wild datasets are not very useful for learning invariance to other parameters forcing researchers to resort to other tricks for training a model. In this work, we in","authors_text":"Ali Borji, Laurent Itti, Saeed Izadi","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-12-04T05:48:09Z","title":"What can we learn about CNNs from a large scale controlled object dataset?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.01320","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":"sha256:4ea79368c6f97b082d95a5b179f677e72e530d07ac53bf5c9d8be67588a9386d","target":"record","created_at":"2026-05-18T01:22:00Z","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":"e9eb5e08f0a95a892cd05d7efc9a016708e3f9174a2a67494c0c15cf8f8d4dc7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-12-04T05:48:09Z","title_canon_sha256":"0dc4e7a6771213e38740fdb773a3b1427d21c362d500c22c35d296652ce3e73a"},"schema_version":"1.0","source":{"id":"1512.01320","kind":"arxiv","version":2}},"canonical_sha256":"45a1e19014f208b19c3f57007f62438c50a9279b58cecfeb764322e5b5926d6e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"45a1e19014f208b19c3f57007f62438c50a9279b58cecfeb764322e5b5926d6e","first_computed_at":"2026-05-18T01:22:00.197065Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:22:00.197065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QLBAa1xmq6GKNUc3zsMY12w8GX7/UOOV9UPiQSnMFuZLQAlmpiNDdbvwBafxKwMnKZXB7riCmazFYwHzN3kJCA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:22:00.197440Z","signed_message":"canonical_sha256_bytes"},"source_id":"1512.01320","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4ea79368c6f97b082d95a5b179f677e72e530d07ac53bf5c9d8be67588a9386d","sha256:e6290ce91fe29edd062dba8a533120a934a7cfe2eb2dfa35da1f0afdda612fc9"],"state_sha256":"e0a3e3094d18dd622086d94ac4ee847bd3a3eb3524e0b8ae35dd595b383fa6b6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZbDw88l9Jf8GOeM6Gbew14aZyKPyZ8z2lspiE8Xv6TtFC+BKGVYe0+ZhAfc7bP5HxmWdPk/1s/TsctkBj9vmBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T11:59:24.022351Z","bundle_sha256":"99e322e1def4d62fbc5d3b1b239f7a1c999ce3134bde261c0db6678c99584002"}}