{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:74MFIDYDI54HJ7ARMWNO7Q7M3W","short_pith_number":"pith:74MFIDYD","canonical_record":{"source":{"id":"1504.03641","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-04-14T17:53:51Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"08a513cfc1b3e181185714ae3eddb5bbc79637430983356f2defbb37edb06877","abstract_canon_sha256":"771e5d2c5c8f4fa3fd82a4f07c10ddb0191c6c2e5df412f8fa3b623498007bbd"},"schema_version":"1.0"},"canonical_sha256":"ff18540f03477874fc11659aefc3ecdd91149892ff78d52301926e55da64d1ea","source":{"kind":"arxiv","id":"1504.03641","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1504.03641","created_at":"2026-05-18T02:18:40Z"},{"alias_kind":"arxiv_version","alias_value":"1504.03641v1","created_at":"2026-05-18T02:18:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.03641","created_at":"2026-05-18T02:18:40Z"},{"alias_kind":"pith_short_12","alias_value":"74MFIDYDI54H","created_at":"2026-05-18T12:29:07Z"},{"alias_kind":"pith_short_16","alias_value":"74MFIDYDI54HJ7AR","created_at":"2026-05-18T12:29:07Z"},{"alias_kind":"pith_short_8","alias_value":"74MFIDYD","created_at":"2026-05-18T12:29:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:74MFIDYDI54HJ7ARMWNO7Q7M3W","target":"record","payload":{"canonical_record":{"source":{"id":"1504.03641","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-04-14T17:53:51Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"08a513cfc1b3e181185714ae3eddb5bbc79637430983356f2defbb37edb06877","abstract_canon_sha256":"771e5d2c5c8f4fa3fd82a4f07c10ddb0191c6c2e5df412f8fa3b623498007bbd"},"schema_version":"1.0"},"canonical_sha256":"ff18540f03477874fc11659aefc3ecdd91149892ff78d52301926e55da64d1ea","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:18:40.187742Z","signature_b64":"VmTh8HTb+1loAeFVQBSTQ6Cz7qdDnL38PaKjdYoUmL+3ssZSEzMprQi6dKSdhHljkehldOjx5m+/YemFz7BIBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff18540f03477874fc11659aefc3ecdd91149892ff78d52301926e55da64d1ea","last_reissued_at":"2026-05-18T02:18:40.187081Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:18:40.187081Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1504.03641","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":"2026-05-18T02:18:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JybkAQLMCZ6PmPr9+O5z0Gqg9YRuGitBaV4sGeDUEU8+tYFqNkstDR6Offa74XIRVwT5u1JU9RZ+DE9EEZnEBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T12:05:36.205444Z"},"content_sha256":"7bffc55669c199e9987edf0ca630e37bb963aa5b1ab443704932b39896631827","schema_version":"1.0","event_id":"sha256:7bffc55669c199e9987edf0ca630e37bb963aa5b1ab443704932b39896631827"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:74MFIDYDI54HJ7ARMWNO7Q7M3W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning to Compare Image Patches via Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CV","authors_text":"Nikos Komodakis, Sergey Zagoruyko","submitted_at":"2015-04-14T17:53:51Z","abstract_excerpt":"In this paper we show how to learn directly from image data (i.e., without resorting to manually-designed features) a general similarity function for comparing image patches, which is a task of fundamental importance for many computer vision problems. To encode such a function, we opt for a CNN-based model that is trained to account for a wide variety of changes in image appearance. To that end, we explore and study multiple neural network architectures, which are specifically adapted to this task. We show that such an approach can significantly outperform the state-of-the-art on several probl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.03641","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":"2026-05-18T02:18:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0dRMcTrN6FQcAw5tk7uCBLp4N+GSjigF322fvCvw8xjRYwikPzNh0ceOzCUDfnn7t0OFU61Z/1S/ULJgmbVtDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T12:05:36.206111Z"},"content_sha256":"833f24a5b8b0e8588552f5151a21ef182bc3844ad680e9f864be20aa649470a3","schema_version":"1.0","event_id":"sha256:833f24a5b8b0e8588552f5151a21ef182bc3844ad680e9f864be20aa649470a3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/74MFIDYDI54HJ7ARMWNO7Q7M3W/bundle.json","state_url":"https://pith.science/pith/74MFIDYDI54HJ7ARMWNO7Q7M3W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/74MFIDYDI54HJ7ARMWNO7Q7M3W/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":"2026-05-30T12:05:36Z","links":{"resolver":"https://pith.science/pith/74MFIDYDI54HJ7ARMWNO7Q7M3W","bundle":"https://pith.science/pith/74MFIDYDI54HJ7ARMWNO7Q7M3W/bundle.json","state":"https://pith.science/pith/74MFIDYDI54HJ7ARMWNO7Q7M3W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/74MFIDYDI54HJ7ARMWNO7Q7M3W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:74MFIDYDI54HJ7ARMWNO7Q7M3W","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":"771e5d2c5c8f4fa3fd82a4f07c10ddb0191c6c2e5df412f8fa3b623498007bbd","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-04-14T17:53:51Z","title_canon_sha256":"08a513cfc1b3e181185714ae3eddb5bbc79637430983356f2defbb37edb06877"},"schema_version":"1.0","source":{"id":"1504.03641","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1504.03641","created_at":"2026-05-18T02:18:40Z"},{"alias_kind":"arxiv_version","alias_value":"1504.03641v1","created_at":"2026-05-18T02:18:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.03641","created_at":"2026-05-18T02:18:40Z"},{"alias_kind":"pith_short_12","alias_value":"74MFIDYDI54H","created_at":"2026-05-18T12:29:07Z"},{"alias_kind":"pith_short_16","alias_value":"74MFIDYDI54HJ7AR","created_at":"2026-05-18T12:29:07Z"},{"alias_kind":"pith_short_8","alias_value":"74MFIDYD","created_at":"2026-05-18T12:29:07Z"}],"graph_snapshots":[{"event_id":"sha256:833f24a5b8b0e8588552f5151a21ef182bc3844ad680e9f864be20aa649470a3","target":"graph","created_at":"2026-05-18T02:18:40Z","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 paper we show how to learn directly from image data (i.e., without resorting to manually-designed features) a general similarity function for comparing image patches, which is a task of fundamental importance for many computer vision problems. To encode such a function, we opt for a CNN-based model that is trained to account for a wide variety of changes in image appearance. To that end, we explore and study multiple neural network architectures, which are specifically adapted to this task. We show that such an approach can significantly outperform the state-of-the-art on several probl","authors_text":"Nikos Komodakis, Sergey Zagoruyko","cross_cats":["cs.LG","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-04-14T17:53:51Z","title":"Learning to Compare Image Patches via Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.03641","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":"sha256:7bffc55669c199e9987edf0ca630e37bb963aa5b1ab443704932b39896631827","target":"record","created_at":"2026-05-18T02:18:40Z","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":"771e5d2c5c8f4fa3fd82a4f07c10ddb0191c6c2e5df412f8fa3b623498007bbd","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-04-14T17:53:51Z","title_canon_sha256":"08a513cfc1b3e181185714ae3eddb5bbc79637430983356f2defbb37edb06877"},"schema_version":"1.0","source":{"id":"1504.03641","kind":"arxiv","version":1}},"canonical_sha256":"ff18540f03477874fc11659aefc3ecdd91149892ff78d52301926e55da64d1ea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ff18540f03477874fc11659aefc3ecdd91149892ff78d52301926e55da64d1ea","first_computed_at":"2026-05-18T02:18:40.187081Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:18:40.187081Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VmTh8HTb+1loAeFVQBSTQ6Cz7qdDnL38PaKjdYoUmL+3ssZSEzMprQi6dKSdhHljkehldOjx5m+/YemFz7BIBA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:18:40.187742Z","signed_message":"canonical_sha256_bytes"},"source_id":"1504.03641","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7bffc55669c199e9987edf0ca630e37bb963aa5b1ab443704932b39896631827","sha256:833f24a5b8b0e8588552f5151a21ef182bc3844ad680e9f864be20aa649470a3"],"state_sha256":"01787cd93febd01e85c7f5ae92202d7a49770f70b03e2e5ab3b32f5711b330e1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NWX0Zd3uNNRhJtbaWe2iAuttCg0e0k1Tq7xDTrMfCfURQ8TYjIm91jiJkSmPTrXWEEAE7ZlQ0k/Jul4aKEpQCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T12:05:36.208898Z","bundle_sha256":"2dc956c390939453140a605a8cfd0aed560b61b048258bb8ce1df29e627071c5"}}