{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:E6OZFKEDFFT6YNNEOVAX7SV74G","short_pith_number":"pith:E6OZFKED","canonical_record":{"source":{"id":"1806.05217","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-13T18:44:10Z","cross_cats_sorted":[],"title_canon_sha256":"2ac1f8e724f6f2ffa3aad27ae670a2812b0aacff8be1687993243b4b5cc97aea","abstract_canon_sha256":"a7f5b4389176289bc6d7509bdb657badca9f9745e70e812c981ac0beebf1e54e"},"schema_version":"1.0"},"canonical_sha256":"279d92a8832967ec35a475417fcabfe197c9677f7601a4ae42b628e10b6eb973","source":{"kind":"arxiv","id":"1806.05217","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.05217","created_at":"2026-05-18T00:13:16Z"},{"alias_kind":"arxiv_version","alias_value":"1806.05217v1","created_at":"2026-05-18T00:13:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.05217","created_at":"2026-05-18T00:13:16Z"},{"alias_kind":"pith_short_12","alias_value":"E6OZFKEDFFT6","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"E6OZFKEDFFT6YNNE","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"E6OZFKED","created_at":"2026-05-18T12:32:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:E6OZFKEDFFT6YNNEOVAX7SV74G","target":"record","payload":{"canonical_record":{"source":{"id":"1806.05217","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-13T18:44:10Z","cross_cats_sorted":[],"title_canon_sha256":"2ac1f8e724f6f2ffa3aad27ae670a2812b0aacff8be1687993243b4b5cc97aea","abstract_canon_sha256":"a7f5b4389176289bc6d7509bdb657badca9f9745e70e812c981ac0beebf1e54e"},"schema_version":"1.0"},"canonical_sha256":"279d92a8832967ec35a475417fcabfe197c9677f7601a4ae42b628e10b6eb973","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:16.797092Z","signature_b64":"KaS1Il1plfFgr9aPAfnFCa/aJ+A3LxYApJgOBuE1/RqMMJswWK83mkBqb7f29ptH1KKbg+K6CgCFtNxsJ6sNBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"279d92a8832967ec35a475417fcabfe197c9677f7601a4ae42b628e10b6eb973","last_reissued_at":"2026-05-18T00:13:16.796418Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:16.796418Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.05217","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":"2026-05-18T00:13:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oN8htFfoxFKLTf+v+ybN538RWNhYpmaaPG7308y0HFsXClTegVgTBXe+mcXcKtFeWJua9pMshjuHDMvbhdPfBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T12:26:55.029921Z"},"content_sha256":"f26cd3f7014b4652f5c1553b0765f689e2de69774060098dfe4954dc687bbca9","schema_version":"1.0","event_id":"sha256:f26cd3f7014b4652f5c1553b0765f689e2de69774060098dfe4954dc687bbca9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:E6OZFKEDFFT6YNNEOVAX7SV74G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Impostor Networks for Fast Fine-Grained Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Artem Babenko, Vadim Lebedev, Victor Lempitsky","submitted_at":"2018-06-13T18:44:10Z","abstract_excerpt":"In this work we introduce impostor networks, an architecture that allows to perform fine-grained recognition with high accuracy and using a light-weight convolutional network, making it particularly suitable for fine-grained applications on low-power and non-GPU enabled platforms. Impostor networks compensate for the lightness of its `backend' network by combining it with a lightweight non-parametric classifier. The combination of a convolutional network and such non-parametric classifier is trained in an end-to-end fashion. Similarly to convolutional neural networks, impostor networks can fit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.05217","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":""},"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":"2026-05-18T00:13:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oHGAjkEIAVMucadrJdB5Id7fU30Iov6FmvqsnK19ZWzbktRxHE+NnVbA9LZpYAdXn/khq6CUzViIYEZd0n4gAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T12:26:55.030540Z"},"content_sha256":"ab53a63c94d895137861b5a3ced86c893f676e5de317d9cbaa386faaa2d6615a","schema_version":"1.0","event_id":"sha256:ab53a63c94d895137861b5a3ced86c893f676e5de317d9cbaa386faaa2d6615a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E6OZFKEDFFT6YNNEOVAX7SV74G/bundle.json","state_url":"https://pith.science/pith/E6OZFKEDFFT6YNNEOVAX7SV74G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E6OZFKEDFFT6YNNEOVAX7SV74G/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":"2026-06-02T12:26:55Z","links":{"resolver":"https://pith.science/pith/E6OZFKEDFFT6YNNEOVAX7SV74G","bundle":"https://pith.science/pith/E6OZFKEDFFT6YNNEOVAX7SV74G/bundle.json","state":"https://pith.science/pith/E6OZFKEDFFT6YNNEOVAX7SV74G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E6OZFKEDFFT6YNNEOVAX7SV74G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:E6OZFKEDFFT6YNNEOVAX7SV74G","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":"a7f5b4389176289bc6d7509bdb657badca9f9745e70e812c981ac0beebf1e54e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-13T18:44:10Z","title_canon_sha256":"2ac1f8e724f6f2ffa3aad27ae670a2812b0aacff8be1687993243b4b5cc97aea"},"schema_version":"1.0","source":{"id":"1806.05217","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.05217","created_at":"2026-05-18T00:13:16Z"},{"alias_kind":"arxiv_version","alias_value":"1806.05217v1","created_at":"2026-05-18T00:13:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.05217","created_at":"2026-05-18T00:13:16Z"},{"alias_kind":"pith_short_12","alias_value":"E6OZFKEDFFT6","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"E6OZFKEDFFT6YNNE","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"E6OZFKED","created_at":"2026-05-18T12:32:19Z"}],"graph_snapshots":[{"event_id":"sha256:ab53a63c94d895137861b5a3ced86c893f676e5de317d9cbaa386faaa2d6615a","target":"graph","created_at":"2026-05-18T00:13:16Z","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 introduce impostor networks, an architecture that allows to perform fine-grained recognition with high accuracy and using a light-weight convolutional network, making it particularly suitable for fine-grained applications on low-power and non-GPU enabled platforms. Impostor networks compensate for the lightness of its `backend' network by combining it with a lightweight non-parametric classifier. The combination of a convolutional network and such non-parametric classifier is trained in an end-to-end fashion. Similarly to convolutional neural networks, impostor networks can fit","authors_text":"Artem Babenko, Vadim Lebedev, Victor Lempitsky","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-13T18:44:10Z","title":"Impostor Networks for Fast Fine-Grained Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.05217","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:f26cd3f7014b4652f5c1553b0765f689e2de69774060098dfe4954dc687bbca9","target":"record","created_at":"2026-05-18T00:13:16Z","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":"a7f5b4389176289bc6d7509bdb657badca9f9745e70e812c981ac0beebf1e54e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-13T18:44:10Z","title_canon_sha256":"2ac1f8e724f6f2ffa3aad27ae670a2812b0aacff8be1687993243b4b5cc97aea"},"schema_version":"1.0","source":{"id":"1806.05217","kind":"arxiv","version":1}},"canonical_sha256":"279d92a8832967ec35a475417fcabfe197c9677f7601a4ae42b628e10b6eb973","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"279d92a8832967ec35a475417fcabfe197c9677f7601a4ae42b628e10b6eb973","first_computed_at":"2026-05-18T00:13:16.796418Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:13:16.796418Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KaS1Il1plfFgr9aPAfnFCa/aJ+A3LxYApJgOBuE1/RqMMJswWK83mkBqb7f29ptH1KKbg+K6CgCFtNxsJ6sNBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:13:16.797092Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.05217","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f26cd3f7014b4652f5c1553b0765f689e2de69774060098dfe4954dc687bbca9","sha256:ab53a63c94d895137861b5a3ced86c893f676e5de317d9cbaa386faaa2d6615a"],"state_sha256":"7d5a9e6564505a21ea0334bbcb303281450b1b034f48f023674cc2aa3c93ba49"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ubu9lPDqY6BhC5Kfa2dJ4RnkCPAocgF6d0LNa6vtsbNHOO/4UhPBk67XuLKNVUGY4NIwg9eCWuDb3ln4iWN/CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T12:26:55.033298Z","bundle_sha256":"748a2bf6e08e7c7a100298e7f53d625583c233646cafddfd5fdc30e0528a3e54"}}