{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:OVJJXWWT4PXLEPWKBNZY7LWR6B","short_pith_number":"pith:OVJJXWWT","canonical_record":{"source":{"id":"1808.00278","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2018-08-01T11:40:59Z","cross_cats_sorted":[],"title_canon_sha256":"a0b3017aa76627517c84c05b7c7a22e2689cfa08f7c74808c6b00b27301e6b7b","abstract_canon_sha256":"60d648b6b9557b0eb9279a90fea16abf653190bdb4b8b00af0cea5cf42bf3e8d"},"schema_version":"1.0"},"canonical_sha256":"75529bdad3e3eeb23eca0b738faed1f05c4eaa153504ba4f30d70173d8a03edc","source":{"kind":"arxiv","id":"1808.00278","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.00278","created_at":"2026-05-18T00:04:29Z"},{"alias_kind":"arxiv_version","alias_value":"1808.00278v5","created_at":"2026-05-18T00:04:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.00278","created_at":"2026-05-18T00:04:29Z"},{"alias_kind":"pith_short_12","alias_value":"OVJJXWWT4PXL","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OVJJXWWT4PXLEPWK","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OVJJXWWT","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:OVJJXWWT4PXLEPWKBNZY7LWR6B","target":"record","payload":{"canonical_record":{"source":{"id":"1808.00278","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2018-08-01T11:40:59Z","cross_cats_sorted":[],"title_canon_sha256":"a0b3017aa76627517c84c05b7c7a22e2689cfa08f7c74808c6b00b27301e6b7b","abstract_canon_sha256":"60d648b6b9557b0eb9279a90fea16abf653190bdb4b8b00af0cea5cf42bf3e8d"},"schema_version":"1.0"},"canonical_sha256":"75529bdad3e3eeb23eca0b738faed1f05c4eaa153504ba4f30d70173d8a03edc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:29.483372Z","signature_b64":"qJFoWYeoZy4G8mE+sPMzxhimUOXDo3nqV/3ozr5uIBEp4sZnOeFTuxAPoBnAzrmu7ejKXlArSdVUFkcFUvBbBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"75529bdad3e3eeb23eca0b738faed1f05c4eaa153504ba4f30d70173d8a03edc","last_reissued_at":"2026-05-18T00:04:29.482934Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:29.482934Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.00278","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:04:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V3it3OCFUrkWBHg3XnpWhY/62T9VU6up/DlnLJDRz3iri7zGU615Di/8cCxi13f1QHMuna2Yw8QT/AQF8C+gAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-18T21:35:26.578408Z"},"content_sha256":"ccb894e3597a623e48c68e033ceb52bb8cdf5dbaba0eb4cf42a88c4d53d80c9f","schema_version":"1.0","event_id":"sha256:ccb894e3597a623e48c68e033ceb52bb8cdf5dbaba0eb4cf42a88c4d53d80c9f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:OVJJXWWT4PXLEPWKBNZY7LWR6B","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Baoyuan Wu, Kwang-Ting Cheng, Wei Liu, Wenhan Luo, Xin Yang, Zechun Liu","submitted_at":"2018-08-01T11:40:59Z","abstract_excerpt":"In this work, we study the 1-bit convolutional neural networks (CNNs), of which both the weights and activations are binary. While being efficient, the classification accuracy of the current 1-bit CNNs is much worse compared to their counterpart real-valued CNN models on the large-scale dataset, like ImageNet. To minimize the performance gap between the 1-bit and real-valued CNN models, we propose a novel model, dubbed Bi-Real net, which connects the real activations (after the 1-bit convolution and/or BatchNorm layer, before the sign function) to activations of the consecutive block, through "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.00278","kind":"arxiv","version":5},"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":"2026-05-18T00:04:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MIYnlGxYG3CqPvYo3aFduY4v+USl9uYIDFyOlmsRE7f4DufEOfQtcqLDBpIAkLnpmfHOvmgK2Y9elOI52KW5Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-18T21:35:26.579079Z"},"content_sha256":"8dedca5cd360d41b5f284359ab0c622543be97635e533ab0aa52e581e8f3acd6","schema_version":"1.0","event_id":"sha256:8dedca5cd360d41b5f284359ab0c622543be97635e533ab0aa52e581e8f3acd6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OVJJXWWT4PXLEPWKBNZY7LWR6B/bundle.json","state_url":"https://pith.science/pith/OVJJXWWT4PXLEPWKBNZY7LWR6B/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OVJJXWWT4PXLEPWKBNZY7LWR6B/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":"2026-05-18T21:35:26Z","links":{"resolver":"https://pith.science/pith/OVJJXWWT4PXLEPWKBNZY7LWR6B","bundle":"https://pith.science/pith/OVJJXWWT4PXLEPWKBNZY7LWR6B/bundle.json","state":"https://pith.science/pith/OVJJXWWT4PXLEPWKBNZY7LWR6B/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OVJJXWWT4PXLEPWKBNZY7LWR6B/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:OVJJXWWT4PXLEPWKBNZY7LWR6B","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":"60d648b6b9557b0eb9279a90fea16abf653190bdb4b8b00af0cea5cf42bf3e8d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2018-08-01T11:40:59Z","title_canon_sha256":"a0b3017aa76627517c84c05b7c7a22e2689cfa08f7c74808c6b00b27301e6b7b"},"schema_version":"1.0","source":{"id":"1808.00278","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.00278","created_at":"2026-05-18T00:04:29Z"},{"alias_kind":"arxiv_version","alias_value":"1808.00278v5","created_at":"2026-05-18T00:04:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.00278","created_at":"2026-05-18T00:04:29Z"},{"alias_kind":"pith_short_12","alias_value":"OVJJXWWT4PXL","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OVJJXWWT4PXLEPWK","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OVJJXWWT","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:8dedca5cd360d41b5f284359ab0c622543be97635e533ab0aa52e581e8f3acd6","target":"graph","created_at":"2026-05-18T00:04:29Z","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 this work, we study the 1-bit convolutional neural networks (CNNs), of which both the weights and activations are binary. While being efficient, the classification accuracy of the current 1-bit CNNs is much worse compared to their counterpart real-valued CNN models on the large-scale dataset, like ImageNet. To minimize the performance gap between the 1-bit and real-valued CNN models, we propose a novel model, dubbed Bi-Real net, which connects the real activations (after the 1-bit convolution and/or BatchNorm layer, before the sign function) to activations of the consecutive block, through ","authors_text":"Baoyuan Wu, Kwang-Ting Cheng, Wei Liu, Wenhan Luo, Xin Yang, Zechun Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2018-08-01T11:40:59Z","title":"Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.00278","kind":"arxiv","version":5},"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:ccb894e3597a623e48c68e033ceb52bb8cdf5dbaba0eb4cf42a88c4d53d80c9f","target":"record","created_at":"2026-05-18T00:04:29Z","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":"60d648b6b9557b0eb9279a90fea16abf653190bdb4b8b00af0cea5cf42bf3e8d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2018-08-01T11:40:59Z","title_canon_sha256":"a0b3017aa76627517c84c05b7c7a22e2689cfa08f7c74808c6b00b27301e6b7b"},"schema_version":"1.0","source":{"id":"1808.00278","kind":"arxiv","version":5}},"canonical_sha256":"75529bdad3e3eeb23eca0b738faed1f05c4eaa153504ba4f30d70173d8a03edc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"75529bdad3e3eeb23eca0b738faed1f05c4eaa153504ba4f30d70173d8a03edc","first_computed_at":"2026-05-18T00:04:29.482934Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:04:29.482934Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qJFoWYeoZy4G8mE+sPMzxhimUOXDo3nqV/3ozr5uIBEp4sZnOeFTuxAPoBnAzrmu7ejKXlArSdVUFkcFUvBbBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:04:29.483372Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.00278","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ccb894e3597a623e48c68e033ceb52bb8cdf5dbaba0eb4cf42a88c4d53d80c9f","sha256:8dedca5cd360d41b5f284359ab0c622543be97635e533ab0aa52e581e8f3acd6"],"state_sha256":"897a5a8293c8b3b8b4e49d8f9652be0e25507a4a5f5c8837c4fd85c47179dec2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3zJt9nGZLTI9kPEIamJTSpv6TJFLdX6rJtvFEaQjgzT7bC64w7F+IgXvXDa4v5f0MBJqCnqW/HrsUPguL/TKCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-18T21:35:26.581117Z","bundle_sha256":"0088d5cebc7381dd3ebd04e408c85cdcf0700e794fa3d10b6b18d464a9804c55"}}