{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:7OLMTF4DW2BJQCFJHW2R4VCZTV","short_pith_number":"pith:7OLMTF4D","canonical_record":{"source":{"id":"1712.00661","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-02T20:25:37Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c62fcdc6f19b008dbe35c4ea615fcda621918e6f24a95d4716b9972898338c12","abstract_canon_sha256":"f604d79da849847c0fdb89352d7e0d395880e99feca754f58ac45a592bd8f747"},"schema_version":"1.0"},"canonical_sha256":"fb96c99783b6829808a93db51e54599d6303ffa0c2323100118b195c73efc225","source":{"kind":"arxiv","id":"1712.00661","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.00661","created_at":"2026-05-18T00:24:54Z"},{"alias_kind":"arxiv_version","alias_value":"1712.00661v3","created_at":"2026-05-18T00:24:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.00661","created_at":"2026-05-18T00:24:54Z"},{"alias_kind":"pith_short_12","alias_value":"7OLMTF4DW2BJ","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"7OLMTF4DW2BJQCFJ","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"7OLMTF4D","created_at":"2026-05-18T12:31:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:7OLMTF4DW2BJQCFJHW2R4VCZTV","target":"record","payload":{"canonical_record":{"source":{"id":"1712.00661","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-02T20:25:37Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c62fcdc6f19b008dbe35c4ea615fcda621918e6f24a95d4716b9972898338c12","abstract_canon_sha256":"f604d79da849847c0fdb89352d7e0d395880e99feca754f58ac45a592bd8f747"},"schema_version":"1.0"},"canonical_sha256":"fb96c99783b6829808a93db51e54599d6303ffa0c2323100118b195c73efc225","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:54.274651Z","signature_b64":"9A6XD9OdvLzwQPTwhe+GHvDlW6hJl2HMOsucJXvthCvz6o26Sfly/BIfo2lD3r0cqlWd4MpQCtPEhZmGOwUeBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fb96c99783b6829808a93db51e54599d6303ffa0c2323100118b195c73efc225","last_reissued_at":"2026-05-18T00:24:54.273878Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:54.273878Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1712.00661","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:24:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UStQYn/KHRpBc4Fx7MPEV50jnbLM1NjH/moIMwZm71a81akvEPsVqtQ4NZ1eTOD3nXcVq6iZJM1uZ5YiFLgHCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T08:41:20.479739Z"},"content_sha256":"bcf2825730b910f4ab2f8615f1dbc38cbfc5176726b1e260ec221b90b6fb652f","schema_version":"1.0","event_id":"sha256:bcf2825730b910f4ab2f8615f1dbc38cbfc5176726b1e260ec221b90b6fb652f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:7OLMTF4DW2BJQCFJHW2R4VCZTV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mix-and-Match Tuning for Self-Supervised Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Chen Change Loy, Ping Luo, Xiaohang Zhan, Xiaoou Tang, Ziwei Liu","submitted_at":"2017-12-02T20:25:37Z","abstract_excerpt":"Deep convolutional networks for semantic image segmentation typically require large-scale labeled data, e.g. ImageNet and MS COCO, for network pre-training. To reduce annotation efforts, self-supervised semantic segmentation is recently proposed to pre-train a network without any human-provided labels. The key of this new form of learning is to design a proxy task (e.g. image colorization), from which a discriminative loss can be formulated on unlabeled data. Many proxy tasks, however, lack the critical supervision signals that could induce discriminative representation for the target image se"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.00661","kind":"arxiv","version":3},"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":"2026-05-18T00:24:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HnJHD4KPF1xJF94pe99E7S1xGV0uUSSSqEGjcuOsbsV3/biDq2QqaH7isMfPUCOf83zjWjuguBk9U6ZgyRwAAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T08:41:20.480070Z"},"content_sha256":"6b32a1e7921b46e49f2b9685b34b86971a86fc22de192f17c4021c51929e6766","schema_version":"1.0","event_id":"sha256:6b32a1e7921b46e49f2b9685b34b86971a86fc22de192f17c4021c51929e6766"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7OLMTF4DW2BJQCFJHW2R4VCZTV/bundle.json","state_url":"https://pith.science/pith/7OLMTF4DW2BJQCFJHW2R4VCZTV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7OLMTF4DW2BJQCFJHW2R4VCZTV/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":"2026-05-30T08:41:20Z","links":{"resolver":"https://pith.science/pith/7OLMTF4DW2BJQCFJHW2R4VCZTV","bundle":"https://pith.science/pith/7OLMTF4DW2BJQCFJHW2R4VCZTV/bundle.json","state":"https://pith.science/pith/7OLMTF4DW2BJQCFJHW2R4VCZTV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7OLMTF4DW2BJQCFJHW2R4VCZTV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:7OLMTF4DW2BJQCFJHW2R4VCZTV","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":"f604d79da849847c0fdb89352d7e0d395880e99feca754f58ac45a592bd8f747","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-02T20:25:37Z","title_canon_sha256":"c62fcdc6f19b008dbe35c4ea615fcda621918e6f24a95d4716b9972898338c12"},"schema_version":"1.0","source":{"id":"1712.00661","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.00661","created_at":"2026-05-18T00:24:54Z"},{"alias_kind":"arxiv_version","alias_value":"1712.00661v3","created_at":"2026-05-18T00:24:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.00661","created_at":"2026-05-18T00:24:54Z"},{"alias_kind":"pith_short_12","alias_value":"7OLMTF4DW2BJ","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"7OLMTF4DW2BJQCFJ","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"7OLMTF4D","created_at":"2026-05-18T12:31:05Z"}],"graph_snapshots":[{"event_id":"sha256:6b32a1e7921b46e49f2b9685b34b86971a86fc22de192f17c4021c51929e6766","target":"graph","created_at":"2026-05-18T00:24:54Z","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":"Deep convolutional networks for semantic image segmentation typically require large-scale labeled data, e.g. ImageNet and MS COCO, for network pre-training. To reduce annotation efforts, self-supervised semantic segmentation is recently proposed to pre-train a network without any human-provided labels. The key of this new form of learning is to design a proxy task (e.g. image colorization), from which a discriminative loss can be formulated on unlabeled data. Many proxy tasks, however, lack the critical supervision signals that could induce discriminative representation for the target image se","authors_text":"Chen Change Loy, Ping Luo, Xiaohang Zhan, Xiaoou Tang, Ziwei Liu","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-02T20:25:37Z","title":"Mix-and-Match Tuning for Self-Supervised Semantic Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.00661","kind":"arxiv","version":3},"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:bcf2825730b910f4ab2f8615f1dbc38cbfc5176726b1e260ec221b90b6fb652f","target":"record","created_at":"2026-05-18T00:24:54Z","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":"f604d79da849847c0fdb89352d7e0d395880e99feca754f58ac45a592bd8f747","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-02T20:25:37Z","title_canon_sha256":"c62fcdc6f19b008dbe35c4ea615fcda621918e6f24a95d4716b9972898338c12"},"schema_version":"1.0","source":{"id":"1712.00661","kind":"arxiv","version":3}},"canonical_sha256":"fb96c99783b6829808a93db51e54599d6303ffa0c2323100118b195c73efc225","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fb96c99783b6829808a93db51e54599d6303ffa0c2323100118b195c73efc225","first_computed_at":"2026-05-18T00:24:54.273878Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:24:54.273878Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9A6XD9OdvLzwQPTwhe+GHvDlW6hJl2HMOsucJXvthCvz6o26Sfly/BIfo2lD3r0cqlWd4MpQCtPEhZmGOwUeBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:24:54.274651Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.00661","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bcf2825730b910f4ab2f8615f1dbc38cbfc5176726b1e260ec221b90b6fb652f","sha256:6b32a1e7921b46e49f2b9685b34b86971a86fc22de192f17c4021c51929e6766"],"state_sha256":"65d746c9a6aa40d75cc028cf46af923e71d3294e31a89065d408899bfa1fad30"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EhubSgJF5VbtAoBTCIC/sw4C5neq6IU+96yI2yd7AkBuu8x1oOfnpWMLLLLyKr9JcKkkOvJOTngTtbhfsPkjBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T08:41:20.482145Z","bundle_sha256":"e68ea1256fee271e7b7a661d93f79f93976ec89ce64c3e3956d59b9d3d371a69"}}