{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:EP7WUQMI5I7OTDNMJPKX3X3ERY","short_pith_number":"pith:EP7WUQMI","canonical_record":{"source":{"id":"1805.03106","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-08T15:29:17Z","cross_cats_sorted":[],"title_canon_sha256":"f73870d97fc39ec672a951fa8e970b934670ec1b3f1dc2bf41b4879285735b85","abstract_canon_sha256":"a077ccea0b7f37721025f4b48b88b99c88adc478d31a46e5aba078579589c619"},"schema_version":"1.0"},"canonical_sha256":"23ff6a4188ea3ee98dac4bd57ddf648e2ec8b92ff388bc7d3853e1b583716da8","source":{"kind":"arxiv","id":"1805.03106","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.03106","created_at":"2026-05-18T00:16:34Z"},{"alias_kind":"arxiv_version","alias_value":"1805.03106v1","created_at":"2026-05-18T00:16:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.03106","created_at":"2026-05-18T00:16:34Z"},{"alias_kind":"pith_short_12","alias_value":"EP7WUQMI5I7O","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"EP7WUQMI5I7OTDNM","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"EP7WUQMI","created_at":"2026-05-18T12:32:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:EP7WUQMI5I7OTDNMJPKX3X3ERY","target":"record","payload":{"canonical_record":{"source":{"id":"1805.03106","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-08T15:29:17Z","cross_cats_sorted":[],"title_canon_sha256":"f73870d97fc39ec672a951fa8e970b934670ec1b3f1dc2bf41b4879285735b85","abstract_canon_sha256":"a077ccea0b7f37721025f4b48b88b99c88adc478d31a46e5aba078579589c619"},"schema_version":"1.0"},"canonical_sha256":"23ff6a4188ea3ee98dac4bd57ddf648e2ec8b92ff388bc7d3853e1b583716da8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:34.569855Z","signature_b64":"zo/CCKJQX7ahgkyAx4rNdssEUE4KE2rXEcN0PTSIu8QcKOhpDKUPR8mJ5cdHukm/6grbp4OYg6Lwfg/5FSwJDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23ff6a4188ea3ee98dac4bd57ddf648e2ec8b92ff388bc7d3853e1b583716da8","last_reissued_at":"2026-05-18T00:16:34.569350Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:34.569350Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.03106","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":"2026-05-18T00:16:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+T6LteIEO/o2myDwnKbcv9oZGyFxnZHAfyizVitGJsMI0HG4PRSvJS5j7gvmmxK+tUstcuBqaH2BnSlQ1EJqAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T18:38:29.246585Z"},"content_sha256":"765524dedba92b0adafbe6c1a9b1aa8a0b65c5c85c691bca71ca3ef4a9565038","schema_version":"1.0","event_id":"sha256:765524dedba92b0adafbe6c1a9b1aa8a0b65c5c85c691bca71ca3ef4a9565038"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:EP7WUQMI5I7OTDNMJPKX3X3ERY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning on the Edge: Explicit Boundary Handling in CNNs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Carlo Innamorati, Niloy J. Mitra, Tim Weyrich, Tobias Ritschel","submitted_at":"2018-05-08T15:29:17Z","abstract_excerpt":"Convolutional neural networks (CNNs) handle the case where filters extend beyond the image boundary using several heuristics, such as zero, repeat or mean padding. These schemes are applied in an ad-hoc fashion and, being weakly related to the image content and oblivious of the target task, result in low output quality at the boundary. In this paper, we propose a simple and effective improvement that learns the boundary handling itself. At training-time, the network is provided with a separate set of explicit boundary filters. At testing-time, we use these filters which have learned to extrapo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.03106","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":"2026-05-18T00:16:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cKi/UpGnaaKhrtvL0Nf6KdAi4UiahuPuNU8xeB+k1Dm905wFlYa0QU3qTYenbCNBsAnAPABPgt0jAiodzeoqDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T18:38:29.247130Z"},"content_sha256":"42bd631b1f35a5f9fd7176264639848866c0447f5942183a78b18b5f87acc303","schema_version":"1.0","event_id":"sha256:42bd631b1f35a5f9fd7176264639848866c0447f5942183a78b18b5f87acc303"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EP7WUQMI5I7OTDNMJPKX3X3ERY/bundle.json","state_url":"https://pith.science/pith/EP7WUQMI5I7OTDNMJPKX3X3ERY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EP7WUQMI5I7OTDNMJPKX3X3ERY/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":"2026-08-03T18:38:29Z","links":{"resolver":"https://pith.science/pith/EP7WUQMI5I7OTDNMJPKX3X3ERY","bundle":"https://pith.science/pith/EP7WUQMI5I7OTDNMJPKX3X3ERY/bundle.json","state":"https://pith.science/pith/EP7WUQMI5I7OTDNMJPKX3X3ERY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EP7WUQMI5I7OTDNMJPKX3X3ERY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:EP7WUQMI5I7OTDNMJPKX3X3ERY","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":"a077ccea0b7f37721025f4b48b88b99c88adc478d31a46e5aba078579589c619","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-08T15:29:17Z","title_canon_sha256":"f73870d97fc39ec672a951fa8e970b934670ec1b3f1dc2bf41b4879285735b85"},"schema_version":"1.0","source":{"id":"1805.03106","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.03106","created_at":"2026-05-18T00:16:34Z"},{"alias_kind":"arxiv_version","alias_value":"1805.03106v1","created_at":"2026-05-18T00:16:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.03106","created_at":"2026-05-18T00:16:34Z"},{"alias_kind":"pith_short_12","alias_value":"EP7WUQMI5I7O","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"EP7WUQMI5I7OTDNM","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"EP7WUQMI","created_at":"2026-05-18T12:32:22Z"}],"graph_snapshots":[{"event_id":"sha256:42bd631b1f35a5f9fd7176264639848866c0447f5942183a78b18b5f87acc303","target":"graph","created_at":"2026-05-18T00:16:34Z","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 neural networks (CNNs) handle the case where filters extend beyond the image boundary using several heuristics, such as zero, repeat or mean padding. These schemes are applied in an ad-hoc fashion and, being weakly related to the image content and oblivious of the target task, result in low output quality at the boundary. In this paper, we propose a simple and effective improvement that learns the boundary handling itself. At training-time, the network is provided with a separate set of explicit boundary filters. At testing-time, we use these filters which have learned to extrapo","authors_text":"Carlo Innamorati, Niloy J. Mitra, Tim Weyrich, Tobias Ritschel","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-08T15:29:17Z","title":"Learning on the Edge: Explicit Boundary Handling in CNNs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.03106","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":"sha256:765524dedba92b0adafbe6c1a9b1aa8a0b65c5c85c691bca71ca3ef4a9565038","target":"record","created_at":"2026-05-18T00:16:34Z","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":"a077ccea0b7f37721025f4b48b88b99c88adc478d31a46e5aba078579589c619","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-08T15:29:17Z","title_canon_sha256":"f73870d97fc39ec672a951fa8e970b934670ec1b3f1dc2bf41b4879285735b85"},"schema_version":"1.0","source":{"id":"1805.03106","kind":"arxiv","version":1}},"canonical_sha256":"23ff6a4188ea3ee98dac4bd57ddf648e2ec8b92ff388bc7d3853e1b583716da8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"23ff6a4188ea3ee98dac4bd57ddf648e2ec8b92ff388bc7d3853e1b583716da8","first_computed_at":"2026-05-18T00:16:34.569350Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:16:34.569350Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zo/CCKJQX7ahgkyAx4rNdssEUE4KE2rXEcN0PTSIu8QcKOhpDKUPR8mJ5cdHukm/6grbp4OYg6Lwfg/5FSwJDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:16:34.569855Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.03106","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:765524dedba92b0adafbe6c1a9b1aa8a0b65c5c85c691bca71ca3ef4a9565038","sha256:42bd631b1f35a5f9fd7176264639848866c0447f5942183a78b18b5f87acc303"],"state_sha256":"c0ca729587910e86627855fae3e51a9dbd8ddca72391a16505585e8359f7a2c5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1DKB8dJmhH4Ti/ji4Q0NZm0xRHXcxe9OF/NOG447Qx7eQfPNdr7sCiaDkX3RUKTHx9CQ4NQfcq9ued1CXSMIDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T18:38:29.252372Z","bundle_sha256":"8d5bf4b9220fc60a9f7cfd5d9883eec850e08acb2c7c830c701c27d995d1373a"}}