{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:QXJO2YJVE2GNJTA2JOGAYXZT6A","short_pith_number":"pith:QXJO2YJV","canonical_record":{"source":{"id":"1512.00567","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-12-02T03:44:38Z","cross_cats_sorted":[],"title_canon_sha256":"0dd0a82f42166f302869594fb0f18882153651353024c71a4cf64bc25b65f0de","abstract_canon_sha256":"e1380745d9227b4633af5807773e52378feddde5688f7e9b7e96a332ca347190"},"schema_version":"1.0"},"canonical_sha256":"85d2ed6135268cd4cc1a4b8c0c5f33f02fe183fae87e9b5a59d5e8f144d70dda","source":{"kind":"arxiv","id":"1512.00567","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1512.00567","created_at":"2026-05-18T01:24:32Z"},{"alias_kind":"arxiv_version","alias_value":"1512.00567v3","created_at":"2026-05-18T01:24:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.00567","created_at":"2026-05-18T01:24:32Z"},{"alias_kind":"pith_short_12","alias_value":"QXJO2YJVE2GN","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_16","alias_value":"QXJO2YJVE2GNJTA2","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_8","alias_value":"QXJO2YJV","created_at":"2026-05-18T12:29:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:QXJO2YJVE2GNJTA2JOGAYXZT6A","target":"record","payload":{"canonical_record":{"source":{"id":"1512.00567","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-12-02T03:44:38Z","cross_cats_sorted":[],"title_canon_sha256":"0dd0a82f42166f302869594fb0f18882153651353024c71a4cf64bc25b65f0de","abstract_canon_sha256":"e1380745d9227b4633af5807773e52378feddde5688f7e9b7e96a332ca347190"},"schema_version":"1.0"},"canonical_sha256":"85d2ed6135268cd4cc1a4b8c0c5f33f02fe183fae87e9b5a59d5e8f144d70dda","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:24:32.531991Z","signature_b64":"aa5ElgXWRbcVkAnpWK7K8zJ/Hos8V/nVso/bPvRcyHphVy8u8aTFBdClMUMqVEZCQc+U5KvkauJLJlObKiZyBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"85d2ed6135268cd4cc1a4b8c0c5f33f02fe183fae87e9b5a59d5e8f144d70dda","last_reissued_at":"2026-05-18T01:24:32.531406Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:24:32.531406Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1512.00567","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":"2026-05-18T01:24:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kKDfa1xoawmT1gAPHCf878EJTLnSfFuIEZTh82n5ga2d4vgYSDrlJOtpuZt0TkTT7v8GhBuMaOMxyU3nE9iMBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T19:03:37.362848Z"},"content_sha256":"4b21a5cd4baff5a65fd373f700a556573e726f6bb8d13fddd2a538780a9e27fe","schema_version":"1.0","event_id":"sha256:4b21a5cd4baff5a65fd373f700a556573e726f6bb8d13fddd2a538780a9e27fe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:QXJO2YJVE2GNJTA2JOGAYXZT6A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Rethinking the Inception Architecture for Computer Vision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Christian Szegedy, Jonathon Shlens, Sergey Ioffe, Vincent Vanhoucke, Zbigniew Wojna","submitted_at":"2015-12-02T03:44:38Z","abstract_excerpt":"Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we explore ways to scale up networks"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.00567","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":"2026-05-18T01:24:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r4pRfhJbVb/l6ZCCzqcGtbhqqBb0vgc4muSRiIlNi0HnaPinSlZqowz3cKKFGI+1DX2GNJcqQmpyCfr0TZVkBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T19:03:37.363630Z"},"content_sha256":"fdbcc9271a88b0aeb35875628562e83b7ba868de3503e739bfe4ddeb0fe6525b","schema_version":"1.0","event_id":"sha256:fdbcc9271a88b0aeb35875628562e83b7ba868de3503e739bfe4ddeb0fe6525b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QXJO2YJVE2GNJTA2JOGAYXZT6A/bundle.json","state_url":"https://pith.science/pith/QXJO2YJVE2GNJTA2JOGAYXZT6A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QXJO2YJVE2GNJTA2JOGAYXZT6A/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":"2026-05-31T19:03:37Z","links":{"resolver":"https://pith.science/pith/QXJO2YJVE2GNJTA2JOGAYXZT6A","bundle":"https://pith.science/pith/QXJO2YJVE2GNJTA2JOGAYXZT6A/bundle.json","state":"https://pith.science/pith/QXJO2YJVE2GNJTA2JOGAYXZT6A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QXJO2YJVE2GNJTA2JOGAYXZT6A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:QXJO2YJVE2GNJTA2JOGAYXZT6A","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":"e1380745d9227b4633af5807773e52378feddde5688f7e9b7e96a332ca347190","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-12-02T03:44:38Z","title_canon_sha256":"0dd0a82f42166f302869594fb0f18882153651353024c71a4cf64bc25b65f0de"},"schema_version":"1.0","source":{"id":"1512.00567","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1512.00567","created_at":"2026-05-18T01:24:32Z"},{"alias_kind":"arxiv_version","alias_value":"1512.00567v3","created_at":"2026-05-18T01:24:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.00567","created_at":"2026-05-18T01:24:32Z"},{"alias_kind":"pith_short_12","alias_value":"QXJO2YJVE2GN","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_16","alias_value":"QXJO2YJVE2GNJTA2","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_8","alias_value":"QXJO2YJV","created_at":"2026-05-18T12:29:39Z"}],"graph_snapshots":[{"event_id":"sha256:fdbcc9271a88b0aeb35875628562e83b7ba868de3503e739bfe4ddeb0fe6525b","target":"graph","created_at":"2026-05-18T01:24:32Z","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"},"paper":{"abstract_excerpt":"Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we explore ways to scale up networks","authors_text":"Christian Szegedy, Jonathon Shlens, Sergey Ioffe, Vincent Vanhoucke, Zbigniew Wojna","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-12-02T03:44:38Z","title":"Rethinking the Inception Architecture for Computer Vision"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.00567","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":"sha256:4b21a5cd4baff5a65fd373f700a556573e726f6bb8d13fddd2a538780a9e27fe","target":"record","created_at":"2026-05-18T01:24:32Z","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":"e1380745d9227b4633af5807773e52378feddde5688f7e9b7e96a332ca347190","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-12-02T03:44:38Z","title_canon_sha256":"0dd0a82f42166f302869594fb0f18882153651353024c71a4cf64bc25b65f0de"},"schema_version":"1.0","source":{"id":"1512.00567","kind":"arxiv","version":3}},"canonical_sha256":"85d2ed6135268cd4cc1a4b8c0c5f33f02fe183fae87e9b5a59d5e8f144d70dda","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"85d2ed6135268cd4cc1a4b8c0c5f33f02fe183fae87e9b5a59d5e8f144d70dda","first_computed_at":"2026-05-18T01:24:32.531406Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:24:32.531406Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aa5ElgXWRbcVkAnpWK7K8zJ/Hos8V/nVso/bPvRcyHphVy8u8aTFBdClMUMqVEZCQc+U5KvkauJLJlObKiZyBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:24:32.531991Z","signed_message":"canonical_sha256_bytes"},"source_id":"1512.00567","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4b21a5cd4baff5a65fd373f700a556573e726f6bb8d13fddd2a538780a9e27fe","sha256:fdbcc9271a88b0aeb35875628562e83b7ba868de3503e739bfe4ddeb0fe6525b"],"state_sha256":"0458e9625fdcad626f9c6425e6606d6d9d12395c9c22feb817ac89a2c9ebbfab"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X1ZkUoH84SNb9GeTiayd2A5w0O1nzoxPJ1OqUX5CRx/qEIGTSx4AlXbu9DcJdUnB+PeCn2RaUS7lwXosairRAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T19:03:37.367565Z","bundle_sha256":"0ec6f4907f8b126d1f74d45f546150fc74d627f556fb998a28ddd10e16e87870"}}