{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:W65STFS6XKAVICQCRRZTQQ42NN","short_pith_number":"pith:W65STFS6","schema_version":"1.0","canonical_sha256":"b7bb29965eba81540a028c7338439a6b7dd71530eb9cb232cd5d793a8a812901","source":{"kind":"arxiv","id":"2010.11426","version":1},"attestation_state":"computed","paper":{"title":"Efficient Scale-Permuted Backbone with Learned Resource Distribution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Mingxing Tan, Pengchong Jin, Quoc Le, Tsung-Yi Lin, Xianzhi Du, Xiaodan Song, Yin Cui","submitted_at":"2020-10-22T03:59:51Z","abstract_excerpt":"Recently, SpineNet has demonstrated promising results on object detection and image classification over ResNet model. However, it is unclear if the improvement adds up when combining scale-permuted backbone with advanced efficient operations and compound scaling. Furthermore, SpineNet is built with a uniform resource distribution over operations. While this strategy seems to be prevalent for scale-decreased models, it may not be an optimal design for scale-permuted models. In this work, we propose a simple technique to combine efficient operations and compound scaling with a previously learned"},"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":"2010.11426","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-10-22T03:59:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"440361b862902d528cfac7f108b112a54eb306db7d0b8b5accd239b39cff2496","abstract_canon_sha256":"cac472a6a9b557bfe010b2f26c5e16d869532feefd69e1c0e0bf735154f5eb89"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:45:10.178008Z","signature_b64":"oEsgtDzwSl5yXWyJJc7DlnxhqEO+27l4ft/1t1a9aR1PpAZ/9QfExBbGlfikDuCdiLkY+TS3JRz9kKEVDYpfDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7bb29965eba81540a028c7338439a6b7dd71530eb9cb232cd5d793a8a812901","last_reissued_at":"2026-07-05T01:45:10.177588Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:45:10.177588Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Scale-Permuted Backbone with Learned Resource Distribution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Mingxing Tan, Pengchong Jin, Quoc Le, Tsung-Yi Lin, Xianzhi Du, Xiaodan Song, Yin Cui","submitted_at":"2020-10-22T03:59:51Z","abstract_excerpt":"Recently, SpineNet has demonstrated promising results on object detection and image classification over ResNet model. However, it is unclear if the improvement adds up when combining scale-permuted backbone with advanced efficient operations and compound scaling. Furthermore, SpineNet is built with a uniform resource distribution over operations. While this strategy seems to be prevalent for scale-decreased models, it may not be an optimal design for scale-permuted models. In this work, we propose a simple technique to combine efficient operations and compound scaling with a previously learned"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.11426","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2010.11426/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2010.11426","created_at":"2026-07-05T01:45:10.177647+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.11426v1","created_at":"2026-07-05T01:45:10.177647+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.11426","created_at":"2026-07-05T01:45:10.177647+00:00"},{"alias_kind":"pith_short_12","alias_value":"W65STFS6XKAV","created_at":"2026-07-05T01:45:10.177647+00:00"},{"alias_kind":"pith_short_16","alias_value":"W65STFS6XKAVICQC","created_at":"2026-07-05T01:45:10.177647+00:00"},{"alias_kind":"pith_short_8","alias_value":"W65STFS6","created_at":"2026-07-05T01:45:10.177647+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/W65STFS6XKAVICQCRRZTQQ42NN","json":"https://pith.science/pith/W65STFS6XKAVICQCRRZTQQ42NN.json","graph_json":"https://pith.science/api/pith-number/W65STFS6XKAVICQCRRZTQQ42NN/graph.json","events_json":"https://pith.science/api/pith-number/W65STFS6XKAVICQCRRZTQQ42NN/events.json","paper":"https://pith.science/paper/W65STFS6"},"agent_actions":{"view_html":"https://pith.science/pith/W65STFS6XKAVICQCRRZTQQ42NN","download_json":"https://pith.science/pith/W65STFS6XKAVICQCRRZTQQ42NN.json","view_paper":"https://pith.science/paper/W65STFS6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.11426&json=true","fetch_graph":"https://pith.science/api/pith-number/W65STFS6XKAVICQCRRZTQQ42NN/graph.json","fetch_events":"https://pith.science/api/pith-number/W65STFS6XKAVICQCRRZTQQ42NN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W65STFS6XKAVICQCRRZTQQ42NN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W65STFS6XKAVICQCRRZTQQ42NN/action/storage_attestation","attest_author":"https://pith.science/pith/W65STFS6XKAVICQCRRZTQQ42NN/action/author_attestation","sign_citation":"https://pith.science/pith/W65STFS6XKAVICQCRRZTQQ42NN/action/citation_signature","submit_replication":"https://pith.science/pith/W65STFS6XKAVICQCRRZTQQ42NN/action/replication_record"}},"created_at":"2026-07-05T01:45:10.177647+00:00","updated_at":"2026-07-05T01:45:10.177647+00:00"}