{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:LOXLJT4RH5ILR3EE22IRZK55SY","short_pith_number":"pith:LOXLJT4R","schema_version":"1.0","canonical_sha256":"5baeb4cf913f50b8ec84d6911cabbd962a7995860ecbbc60fdf8e484e9f31302","source":{"kind":"arxiv","id":"1809.03355","version":1},"attestation_state":"computed","paper":{"title":"Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adri\\`a Recasens, Antonio Torralba, Petr Kellnhofer, Simon Stent, Wojciech Matusik","submitted_at":"2018-09-10T14:36:15Z","abstract_excerpt":"We introduce a saliency-based distortion layer for convolutional neural networks that helps to improve the spatial sampling of input data for a given task. Our differentiable layer can be added as a preprocessing block to existing task networks and trained altogether in an end-to-end fashion. The effect of the layer is to efficiently estimate how to sample from the original data in order to boost task performance. For example, for an image classification task in which the original data might range in size up to several megapixels, but where the desired input images to the task network are much"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1809.03355","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-10T14:36:15Z","cross_cats_sorted":[],"title_canon_sha256":"7d74821d374d40283c1a23739f3ba47844a7132a8b43265c0bddaad707d78830","abstract_canon_sha256":"9d0703450d6f18d98baf07bb96779793d09d906969d0c1e7f52520c69fba456f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:07.107650Z","signature_b64":"FxIvrx4ob1MDpIjYKcVZ/jhE3qBpllKE5PcQ5BrP+fCw64jH0LKwPHZ/M5WdlZ/mSlmuwoqZNBNw/FMq49KvBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5baeb4cf913f50b8ec84d6911cabbd962a7995860ecbbc60fdf8e484e9f31302","last_reissued_at":"2026-05-18T00:06:07.107062Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:07.107062Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adri\\`a Recasens, Antonio Torralba, Petr Kellnhofer, Simon Stent, Wojciech Matusik","submitted_at":"2018-09-10T14:36:15Z","abstract_excerpt":"We introduce a saliency-based distortion layer for convolutional neural networks that helps to improve the spatial sampling of input data for a given task. Our differentiable layer can be added as a preprocessing block to existing task networks and trained altogether in an end-to-end fashion. The effect of the layer is to efficiently estimate how to sample from the original data in order to boost task performance. For example, for an image classification task in which the original data might range in size up to several megapixels, but where the desired input images to the task network are much"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.03355","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1809.03355","created_at":"2026-05-18T00:06:07.107145+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.03355v1","created_at":"2026-05-18T00:06:07.107145+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.03355","created_at":"2026-05-18T00:06:07.107145+00:00"},{"alias_kind":"pith_short_12","alias_value":"LOXLJT4RH5IL","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_16","alias_value":"LOXLJT4RH5ILR3EE","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_8","alias_value":"LOXLJT4R","created_at":"2026-05-18T12:32:37.024351+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LOXLJT4RH5ILR3EE22IRZK55SY","json":"https://pith.science/pith/LOXLJT4RH5ILR3EE22IRZK55SY.json","graph_json":"https://pith.science/api/pith-number/LOXLJT4RH5ILR3EE22IRZK55SY/graph.json","events_json":"https://pith.science/api/pith-number/LOXLJT4RH5ILR3EE22IRZK55SY/events.json","paper":"https://pith.science/paper/LOXLJT4R"},"agent_actions":{"view_html":"https://pith.science/pith/LOXLJT4RH5ILR3EE22IRZK55SY","download_json":"https://pith.science/pith/LOXLJT4RH5ILR3EE22IRZK55SY.json","view_paper":"https://pith.science/paper/LOXLJT4R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.03355&json=true","fetch_graph":"https://pith.science/api/pith-number/LOXLJT4RH5ILR3EE22IRZK55SY/graph.json","fetch_events":"https://pith.science/api/pith-number/LOXLJT4RH5ILR3EE22IRZK55SY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LOXLJT4RH5ILR3EE22IRZK55SY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LOXLJT4RH5ILR3EE22IRZK55SY/action/storage_attestation","attest_author":"https://pith.science/pith/LOXLJT4RH5ILR3EE22IRZK55SY/action/author_attestation","sign_citation":"https://pith.science/pith/LOXLJT4RH5ILR3EE22IRZK55SY/action/citation_signature","submit_replication":"https://pith.science/pith/LOXLJT4RH5ILR3EE22IRZK55SY/action/replication_record"}},"created_at":"2026-05-18T00:06:07.107145+00:00","updated_at":"2026-05-18T00:06:07.107145+00:00"}