{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:Z7B7MPSEYDLSDANTTPMUV4HZJK","short_pith_number":"pith:Z7B7MPSE","canonical_record":{"source":{"id":"2209.13444","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-09-27T15:04:31Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"921e1ccba722b0e3cacf8406591f89ae0dc7c767522b49ef5b1d2abfab38b97f","abstract_canon_sha256":"4a499236cb4b5f1adb4db793f68774da0504fc6be96858a5c3c54d34f0eee3b8"},"schema_version":"1.0"},"canonical_sha256":"cfc3f63e44c0d72181b39bd94af0f94ab2091f8814ce218f1608f26dfb77ca8c","source":{"kind":"arxiv","id":"2209.13444","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.13444","created_at":"2026-07-05T05:01:32Z"},{"alias_kind":"arxiv_version","alias_value":"2209.13444v1","created_at":"2026-07-05T05:01:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.13444","created_at":"2026-07-05T05:01:32Z"},{"alias_kind":"pith_short_12","alias_value":"Z7B7MPSEYDLS","created_at":"2026-07-05T05:01:32Z"},{"alias_kind":"pith_short_16","alias_value":"Z7B7MPSEYDLSDANT","created_at":"2026-07-05T05:01:32Z"},{"alias_kind":"pith_short_8","alias_value":"Z7B7MPSE","created_at":"2026-07-05T05:01:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:Z7B7MPSEYDLSDANTTPMUV4HZJK","target":"record","payload":{"canonical_record":{"source":{"id":"2209.13444","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-09-27T15:04:31Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"921e1ccba722b0e3cacf8406591f89ae0dc7c767522b49ef5b1d2abfab38b97f","abstract_canon_sha256":"4a499236cb4b5f1adb4db793f68774da0504fc6be96858a5c3c54d34f0eee3b8"},"schema_version":"1.0"},"canonical_sha256":"cfc3f63e44c0d72181b39bd94af0f94ab2091f8814ce218f1608f26dfb77ca8c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:01:32.732652Z","signature_b64":"3zY0QJ99Cpq0yCMit+TNio8LnY1oP2CIurcJ6fCYss1UQPehTPi4KMT50kPpUev0TDldInaCK+fPIGcGbKdBAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cfc3f63e44c0d72181b39bd94af0f94ab2091f8814ce218f1608f26dfb77ca8c","last_reissued_at":"2026-07-05T05:01:32.732239Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:01:32.732239Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2209.13444","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":"2026-07-05T05:01:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5Dwl0PXbsPByMH8eZxSqgTUY1eoDEFUMmFc+6WJQQ+N0gY3hlnoRYAgeAUtpklwnVliAM77ROkynYmmxNgf6DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T21:45:40.352694Z"},"content_sha256":"a7c49994d87f842663369147deb6b310534377a7c89a5eeb7335113365eec9e2","schema_version":"1.0","event_id":"sha256:a7c49994d87f842663369147deb6b310534377a7c89a5eeb7335113365eec9e2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:Z7B7MPSEYDLSDANTTPMUV4HZJK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Design Perspectives of Multitask Deep Learning Models and Applications","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Angshuman Bora, Anupam Biswas, Debashish Malakar, Subham Chakraborty, Suman Bera, Yeshwant Singh","submitted_at":"2022-09-27T15:04:31Z","abstract_excerpt":"In recent years, multi-task learning has turned out to be of great success in various applications. Though single model training has promised great results throughout these years, it ignores valuable information that might help us estimate a metric better. Under learning-related tasks, multi-task learning has been able to generalize the models even better. We try to enhance the feature mapping of the multi-tasking models by sharing features among related tasks and inductive transfer learning. Also, our interest is in learning the task relationships among various tasks for acquiring better bene"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.13444","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/2209.13444/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":"2026-07-05T05:01:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t0t+bpyQiPVVhc02Rq5QsuoKNSsizJi1BAD+klaE4ld6b8IOCq8oyVcfK+RyNIihECOpcc8Y8hZLYAR95eczCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T21:45:40.353197Z"},"content_sha256":"71bdbe046b9e7430a2860a562c386835a764ccf0afe4049486b0a479b2a82bf6","schema_version":"1.0","event_id":"sha256:71bdbe046b9e7430a2860a562c386835a764ccf0afe4049486b0a479b2a82bf6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Z7B7MPSEYDLSDANTTPMUV4HZJK/bundle.json","state_url":"https://pith.science/pith/Z7B7MPSEYDLSDANTTPMUV4HZJK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Z7B7MPSEYDLSDANTTPMUV4HZJK/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":"2026-08-05T21:45:40Z","links":{"resolver":"https://pith.science/pith/Z7B7MPSEYDLSDANTTPMUV4HZJK","bundle":"https://pith.science/pith/Z7B7MPSEYDLSDANTTPMUV4HZJK/bundle.json","state":"https://pith.science/pith/Z7B7MPSEYDLSDANTTPMUV4HZJK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Z7B7MPSEYDLSDANTTPMUV4HZJK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:Z7B7MPSEYDLSDANTTPMUV4HZJK","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":"4a499236cb4b5f1adb4db793f68774da0504fc6be96858a5c3c54d34f0eee3b8","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-09-27T15:04:31Z","title_canon_sha256":"921e1ccba722b0e3cacf8406591f89ae0dc7c767522b49ef5b1d2abfab38b97f"},"schema_version":"1.0","source":{"id":"2209.13444","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.13444","created_at":"2026-07-05T05:01:32Z"},{"alias_kind":"arxiv_version","alias_value":"2209.13444v1","created_at":"2026-07-05T05:01:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.13444","created_at":"2026-07-05T05:01:32Z"},{"alias_kind":"pith_short_12","alias_value":"Z7B7MPSEYDLS","created_at":"2026-07-05T05:01:32Z"},{"alias_kind":"pith_short_16","alias_value":"Z7B7MPSEYDLSDANT","created_at":"2026-07-05T05:01:32Z"},{"alias_kind":"pith_short_8","alias_value":"Z7B7MPSE","created_at":"2026-07-05T05:01:32Z"}],"graph_snapshots":[{"event_id":"sha256:71bdbe046b9e7430a2860a562c386835a764ccf0afe4049486b0a479b2a82bf6","target":"graph","created_at":"2026-07-05T05:01:32Z","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/2209.13444/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, multi-task learning has turned out to be of great success in various applications. Though single model training has promised great results throughout these years, it ignores valuable information that might help us estimate a metric better. Under learning-related tasks, multi-task learning has been able to generalize the models even better. We try to enhance the feature mapping of the multi-tasking models by sharing features among related tasks and inductive transfer learning. Also, our interest is in learning the task relationships among various tasks for acquiring better bene","authors_text":"Angshuman Bora, Anupam Biswas, Debashish Malakar, Subham Chakraborty, Suman Bera, Yeshwant Singh","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-09-27T15:04:31Z","title":"Design Perspectives of Multitask Deep Learning Models and Applications"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.13444","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:a7c49994d87f842663369147deb6b310534377a7c89a5eeb7335113365eec9e2","target":"record","created_at":"2026-07-05T05:01:32Z","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":"4a499236cb4b5f1adb4db793f68774da0504fc6be96858a5c3c54d34f0eee3b8","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-09-27T15:04:31Z","title_canon_sha256":"921e1ccba722b0e3cacf8406591f89ae0dc7c767522b49ef5b1d2abfab38b97f"},"schema_version":"1.0","source":{"id":"2209.13444","kind":"arxiv","version":1}},"canonical_sha256":"cfc3f63e44c0d72181b39bd94af0f94ab2091f8814ce218f1608f26dfb77ca8c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cfc3f63e44c0d72181b39bd94af0f94ab2091f8814ce218f1608f26dfb77ca8c","first_computed_at":"2026-07-05T05:01:32.732239Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:01:32.732239Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3zY0QJ99Cpq0yCMit+TNio8LnY1oP2CIurcJ6fCYss1UQPehTPi4KMT50kPpUev0TDldInaCK+fPIGcGbKdBAg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:01:32.732652Z","signed_message":"canonical_sha256_bytes"},"source_id":"2209.13444","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a7c49994d87f842663369147deb6b310534377a7c89a5eeb7335113365eec9e2","sha256:71bdbe046b9e7430a2860a562c386835a764ccf0afe4049486b0a479b2a82bf6"],"state_sha256":"ee650d709b64a3fca879ed21dcdf68f4eba9e84830089e90ef9edaf6768cfd1b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yxwIbtO6CItKlba8M5vYhGbylEzpvzAsLu5RfC7l8N0Lc05jk8FIr9SgtF+f6sKP7b0tTI+g7Kx1JsuFVWtpCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T21:45:40.358270Z","bundle_sha256":"ce1d5e9c4936d5258fadba052f87007e58de96d96700acc313714c87627322d0"}}