{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:CMA5IXU5A3BS7UHF22B2IX4XLY","short_pith_number":"pith:CMA5IXU5","canonical_record":{"source":{"id":"2312.00950","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-01T22:03:25Z","cross_cats_sorted":[],"title_canon_sha256":"9e976ccff44ee47d2cc26e37583b545c795f04db17fde19dccc3c0c64dba6dea","abstract_canon_sha256":"82477dadc6f02b7a328ca303a274fb5d43b7e70079e2e611ff79c1189c198edc"},"schema_version":"1.0"},"canonical_sha256":"1301d45e9d06c32fd0e5d683a45f975e157dc7a234489b46ff8f4b620463d9b9","source":{"kind":"arxiv","id":"2312.00950","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.00950","created_at":"2026-07-05T07:19:26Z"},{"alias_kind":"arxiv_version","alias_value":"2312.00950v1","created_at":"2026-07-05T07:19:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.00950","created_at":"2026-07-05T07:19:26Z"},{"alias_kind":"pith_short_12","alias_value":"CMA5IXU5A3BS","created_at":"2026-07-05T07:19:26Z"},{"alias_kind":"pith_short_16","alias_value":"CMA5IXU5A3BS7UHF","created_at":"2026-07-05T07:19:26Z"},{"alias_kind":"pith_short_8","alias_value":"CMA5IXU5","created_at":"2026-07-05T07:19:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:CMA5IXU5A3BS7UHF22B2IX4XLY","target":"record","payload":{"canonical_record":{"source":{"id":"2312.00950","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-01T22:03:25Z","cross_cats_sorted":[],"title_canon_sha256":"9e976ccff44ee47d2cc26e37583b545c795f04db17fde19dccc3c0c64dba6dea","abstract_canon_sha256":"82477dadc6f02b7a328ca303a274fb5d43b7e70079e2e611ff79c1189c198edc"},"schema_version":"1.0"},"canonical_sha256":"1301d45e9d06c32fd0e5d683a45f975e157dc7a234489b46ff8f4b620463d9b9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:19:26.997164Z","signature_b64":"uPdvaTVBERQ7W4F2ZQRJnlL4PoWd8/FsvCVrH/ctsQ1uH8ZB+IX2RKkprVQvOuv/djxKgyVsFpvx7ukkoHtyAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1301d45e9d06c32fd0e5d683a45f975e157dc7a234489b46ff8f4b620463d9b9","last_reissued_at":"2026-07-05T07:19:26.996770Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:19:26.996770Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.00950","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":"2026-07-05T07:19:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QMfNTkxNdWQle2bZ1vpET8Sko0Ubh5cAeNHHHAsdJNGibtitBn0xK2aOqhazBZ8GoWi/ZuQZf9pJqfvWUfMvCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T05:03:53.512350Z"},"content_sha256":"201659c2646a2efeebbe13fcb37bd2cbb160e4bc82a1e611091330f6a152ba1b","schema_version":"1.0","event_id":"sha256:201659c2646a2efeebbe13fcb37bd2cbb160e4bc82a1e611091330f6a152ba1b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:CMA5IXU5A3BS7UHF22B2IX4XLY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improve Supervised Representation Learning with Masked Image Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Daniel Salz, Dilip Krishnan, Huiwen Chang, Kaifeng Chen, Kihyuk Sohn, Mojtaba Seyedhosseini","submitted_at":"2023-12-01T22:03:25Z","abstract_excerpt":"Training visual embeddings with labeled data supervision has been the de facto setup for representation learning in computer vision. Inspired by recent success of adopting masked image modeling (MIM) in self-supervised representation learning, we propose a simple yet effective setup that can easily integrate MIM into existing supervised training paradigms. In our design, in addition to the original classification task applied to a vision transformer image encoder, we add a shallow transformer-based decoder on top of the encoder and introduce an MIM task which tries to reconstruct image tokens "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.00950","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/2312.00950/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":"2026-07-05T07:19:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KvqgmZxtxRhYkxq/+qJ99G3KnSJNDWeZodyFd64GRcCGz7myrWPwHsRf6KaYHyz/FcyI2/diBxCFhiovnCElCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T05:03:53.512967Z"},"content_sha256":"465074d9746bdf2bf8d28a64cb68b0ec88811e9529c9d3f2fe8d2f2e83a9e2e6","schema_version":"1.0","event_id":"sha256:465074d9746bdf2bf8d28a64cb68b0ec88811e9529c9d3f2fe8d2f2e83a9e2e6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CMA5IXU5A3BS7UHF22B2IX4XLY/bundle.json","state_url":"https://pith.science/pith/CMA5IXU5A3BS7UHF22B2IX4XLY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CMA5IXU5A3BS7UHF22B2IX4XLY/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":"2026-07-07T05:03:53Z","links":{"resolver":"https://pith.science/pith/CMA5IXU5A3BS7UHF22B2IX4XLY","bundle":"https://pith.science/pith/CMA5IXU5A3BS7UHF22B2IX4XLY/bundle.json","state":"https://pith.science/pith/CMA5IXU5A3BS7UHF22B2IX4XLY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CMA5IXU5A3BS7UHF22B2IX4XLY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:CMA5IXU5A3BS7UHF22B2IX4XLY","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":"82477dadc6f02b7a328ca303a274fb5d43b7e70079e2e611ff79c1189c198edc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-01T22:03:25Z","title_canon_sha256":"9e976ccff44ee47d2cc26e37583b545c795f04db17fde19dccc3c0c64dba6dea"},"schema_version":"1.0","source":{"id":"2312.00950","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.00950","created_at":"2026-07-05T07:19:26Z"},{"alias_kind":"arxiv_version","alias_value":"2312.00950v1","created_at":"2026-07-05T07:19:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.00950","created_at":"2026-07-05T07:19:26Z"},{"alias_kind":"pith_short_12","alias_value":"CMA5IXU5A3BS","created_at":"2026-07-05T07:19:26Z"},{"alias_kind":"pith_short_16","alias_value":"CMA5IXU5A3BS7UHF","created_at":"2026-07-05T07:19:26Z"},{"alias_kind":"pith_short_8","alias_value":"CMA5IXU5","created_at":"2026-07-05T07:19:26Z"}],"graph_snapshots":[{"event_id":"sha256:465074d9746bdf2bf8d28a64cb68b0ec88811e9529c9d3f2fe8d2f2e83a9e2e6","target":"graph","created_at":"2026-07-05T07:19:26Z","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/2312.00950/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Training visual embeddings with labeled data supervision has been the de facto setup for representation learning in computer vision. Inspired by recent success of adopting masked image modeling (MIM) in self-supervised representation learning, we propose a simple yet effective setup that can easily integrate MIM into existing supervised training paradigms. In our design, in addition to the original classification task applied to a vision transformer image encoder, we add a shallow transformer-based decoder on top of the encoder and introduce an MIM task which tries to reconstruct image tokens ","authors_text":"Daniel Salz, Dilip Krishnan, Huiwen Chang, Kaifeng Chen, Kihyuk Sohn, Mojtaba Seyedhosseini","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-01T22:03:25Z","title":"Improve Supervised Representation Learning with Masked Image Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.00950","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":"sha256:201659c2646a2efeebbe13fcb37bd2cbb160e4bc82a1e611091330f6a152ba1b","target":"record","created_at":"2026-07-05T07:19:26Z","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":"82477dadc6f02b7a328ca303a274fb5d43b7e70079e2e611ff79c1189c198edc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-01T22:03:25Z","title_canon_sha256":"9e976ccff44ee47d2cc26e37583b545c795f04db17fde19dccc3c0c64dba6dea"},"schema_version":"1.0","source":{"id":"2312.00950","kind":"arxiv","version":1}},"canonical_sha256":"1301d45e9d06c32fd0e5d683a45f975e157dc7a234489b46ff8f4b620463d9b9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1301d45e9d06c32fd0e5d683a45f975e157dc7a234489b46ff8f4b620463d9b9","first_computed_at":"2026-07-05T07:19:26.996770Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:19:26.996770Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uPdvaTVBERQ7W4F2ZQRJnlL4PoWd8/FsvCVrH/ctsQ1uH8ZB+IX2RKkprVQvOuv/djxKgyVsFpvx7ukkoHtyAg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:19:26.997164Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.00950","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:201659c2646a2efeebbe13fcb37bd2cbb160e4bc82a1e611091330f6a152ba1b","sha256:465074d9746bdf2bf8d28a64cb68b0ec88811e9529c9d3f2fe8d2f2e83a9e2e6"],"state_sha256":"b4b5be5c290bfdafd5e55f4f9ce80bf2bf1f251c098c879e0941f333a3da615e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LWsmx+zWn6HVtLbO4KnnVtP3XwS88H+xSpAwxdzE5821Jp3i2iWCSA+HSUIfjBYdTbIXsvktaK0mCeu3zJ4mAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T05:03:53.516413Z","bundle_sha256":"cc95acd926e4a637d853e8530a9682ffcf1484bb94f5b20ca0ff6b7e94ce625c"}}