{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:YGR4BVVZFAWZIRIV476ARA2ONN","short_pith_number":"pith:YGR4BVVZ","canonical_record":{"source":{"id":"1611.05916","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-17T22:03:35Z","cross_cats_sorted":[],"title_canon_sha256":"2178a690226c3eaf4cb27fd2f8ad827ec1c291b680bbffa772b48326090487d6","abstract_canon_sha256":"d6bb6b63db30175a8ced79244a7b59b993926d154160b5bdacd1b66801e8e223"},"schema_version":"1.0"},"canonical_sha256":"c1a3c0d6b9282d944515e7fc08834e6b60aae67875e8e7b5e0c846ca0707a65b","source":{"kind":"arxiv","id":"1611.05916","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.05916","created_at":"2026-05-18T00:47:26Z"},{"alias_kind":"arxiv_version","alias_value":"1611.05916v4","created_at":"2026-05-18T00:47:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.05916","created_at":"2026-05-18T00:47:26Z"},{"alias_kind":"pith_short_12","alias_value":"YGR4BVVZFAWZ","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"YGR4BVVZFAWZIRIV","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"YGR4BVVZ","created_at":"2026-05-18T12:30:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:YGR4BVVZFAWZIRIV476ARA2ONN","target":"record","payload":{"canonical_record":{"source":{"id":"1611.05916","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-17T22:03:35Z","cross_cats_sorted":[],"title_canon_sha256":"2178a690226c3eaf4cb27fd2f8ad827ec1c291b680bbffa772b48326090487d6","abstract_canon_sha256":"d6bb6b63db30175a8ced79244a7b59b993926d154160b5bdacd1b66801e8e223"},"schema_version":"1.0"},"canonical_sha256":"c1a3c0d6b9282d944515e7fc08834e6b60aae67875e8e7b5e0c846ca0707a65b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:47:26.630270Z","signature_b64":"Jx3w2GGdDjQkS/cPhkRZTh1J5TXGYubgWe/xrSmeoAhq9eM6N1p9Ne5l+nTAGawZPih21zrXmT/hYu8CPekwCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c1a3c0d6b9282d944515e7fc08834e6b60aae67875e8e7b5e0c846ca0707a65b","last_reissued_at":"2026-05-18T00:47:26.629627Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:47:26.629627Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.05916","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:47:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2i/jacoo8dRx5e7lz+G6dVRCg+f3CD3md37oTAi1gPFOIf6VsGwNTceQ38V9PjfswQQnvhiyLGBkwrpYdPaPCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T17:59:31.427309Z"},"content_sha256":"c8244bdf811b2bacce584768abe98b7d4515f16cff7d591ba4bc7351ddefaca6","schema_version":"1.0","event_id":"sha256:c8244bdf811b2bacce584768abe98b7d4515f16cff7d591ba4bc7351ddefaca6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:YGR4BVVZFAWZIRIV476ARA2ONN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen-Ping Yu, Dimitris Samaras, Le Hou","submitted_at":"2016-11-17T22:03:35Z","abstract_excerpt":"In the context of single-label classification, despite the huge success of deep learning, the commonly used cross-entropy loss function ignores the intricate inter-class relationships that often exist in real-life tasks such as age classification. In this work, we propose to leverage these relationships between classes by training deep nets with the exact squared Earth Mover's Distance (also known as Wasserstein distance) for single-label classification. The squared EMD loss uses the predicted probabilities of all classes and penalizes the miss-predictions according to a ground distance matrix"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.05916","kind":"arxiv","version":4},"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":"2026-05-18T00:47:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oZxS2vWFWOwPzh4RAhp6Mjb68nNH6QpZTBy2kpOvLE+d5S5L9TYVc+wf8pX01czlr+XIhO4q72OxnYUIadMSCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T17:59:31.427700Z"},"content_sha256":"303a8130c84aa22f2438b628a7a341a851485df79ebc008a7f5fa2ad3d0ff193","schema_version":"1.0","event_id":"sha256:303a8130c84aa22f2438b628a7a341a851485df79ebc008a7f5fa2ad3d0ff193"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YGR4BVVZFAWZIRIV476ARA2ONN/bundle.json","state_url":"https://pith.science/pith/YGR4BVVZFAWZIRIV476ARA2ONN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YGR4BVVZFAWZIRIV476ARA2ONN/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":"2026-06-03T17:59:31Z","links":{"resolver":"https://pith.science/pith/YGR4BVVZFAWZIRIV476ARA2ONN","bundle":"https://pith.science/pith/YGR4BVVZFAWZIRIV476ARA2ONN/bundle.json","state":"https://pith.science/pith/YGR4BVVZFAWZIRIV476ARA2ONN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YGR4BVVZFAWZIRIV476ARA2ONN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:YGR4BVVZFAWZIRIV476ARA2ONN","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":"d6bb6b63db30175a8ced79244a7b59b993926d154160b5bdacd1b66801e8e223","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-17T22:03:35Z","title_canon_sha256":"2178a690226c3eaf4cb27fd2f8ad827ec1c291b680bbffa772b48326090487d6"},"schema_version":"1.0","source":{"id":"1611.05916","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.05916","created_at":"2026-05-18T00:47:26Z"},{"alias_kind":"arxiv_version","alias_value":"1611.05916v4","created_at":"2026-05-18T00:47:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.05916","created_at":"2026-05-18T00:47:26Z"},{"alias_kind":"pith_short_12","alias_value":"YGR4BVVZFAWZ","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"YGR4BVVZFAWZIRIV","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"YGR4BVVZ","created_at":"2026-05-18T12:30:53Z"}],"graph_snapshots":[{"event_id":"sha256:303a8130c84aa22f2438b628a7a341a851485df79ebc008a7f5fa2ad3d0ff193","target":"graph","created_at":"2026-05-18T00:47:26Z","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"},"paper":{"abstract_excerpt":"In the context of single-label classification, despite the huge success of deep learning, the commonly used cross-entropy loss function ignores the intricate inter-class relationships that often exist in real-life tasks such as age classification. In this work, we propose to leverage these relationships between classes by training deep nets with the exact squared Earth Mover's Distance (also known as Wasserstein distance) for single-label classification. The squared EMD loss uses the predicted probabilities of all classes and penalizes the miss-predictions according to a ground distance matrix","authors_text":"Chen-Ping Yu, Dimitris Samaras, Le Hou","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-17T22:03:35Z","title":"Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.05916","kind":"arxiv","version":4},"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:c8244bdf811b2bacce584768abe98b7d4515f16cff7d591ba4bc7351ddefaca6","target":"record","created_at":"2026-05-18T00:47:26Z","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":"d6bb6b63db30175a8ced79244a7b59b993926d154160b5bdacd1b66801e8e223","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-17T22:03:35Z","title_canon_sha256":"2178a690226c3eaf4cb27fd2f8ad827ec1c291b680bbffa772b48326090487d6"},"schema_version":"1.0","source":{"id":"1611.05916","kind":"arxiv","version":4}},"canonical_sha256":"c1a3c0d6b9282d944515e7fc08834e6b60aae67875e8e7b5e0c846ca0707a65b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c1a3c0d6b9282d944515e7fc08834e6b60aae67875e8e7b5e0c846ca0707a65b","first_computed_at":"2026-05-18T00:47:26.629627Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:47:26.629627Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Jx3w2GGdDjQkS/cPhkRZTh1J5TXGYubgWe/xrSmeoAhq9eM6N1p9Ne5l+nTAGawZPih21zrXmT/hYu8CPekwCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:47:26.630270Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.05916","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c8244bdf811b2bacce584768abe98b7d4515f16cff7d591ba4bc7351ddefaca6","sha256:303a8130c84aa22f2438b628a7a341a851485df79ebc008a7f5fa2ad3d0ff193"],"state_sha256":"2ee44068d1000ed32e95b5852eaae1c1eff6620d4fe458bbadf1a0b50410f701"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1AK4+4RWaYDZO6jA7Ki0ikSeFV/q/qRVob22S90g/lq2bi8Iw2ErJolytTX5RceKlxh+HNJvJRA5GzMWGQ8PBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T17:59:31.429621Z","bundle_sha256":"2782038557480c861431a77b35fed7cdab9e5c9726adba99d358063d09c5201f"}}