{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LRXQCEQNWDTPHADZTQK66RXVLG","short_pith_number":"pith:LRXQCEQN","schema_version":"1.0","canonical_sha256":"5c6f01120db0e6f380799c15ef46f559870b9794e3a11f8c9f17b9d206cd43e9","source":{"kind":"arxiv","id":"2606.02927","version":1},"attestation_state":"computed","paper":{"title":"SaluNet: Enabling Total Plasticity in Normalization-Free Deep Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mourad Zaied (University of Gabes, Tuisia)","submitted_at":"2026-06-01T22:09:06Z","abstract_excerpt":"Normalization layers such as BatchNorm and LayerNorm have long been considered essential for stable training in deep networks. This work demonstrates that they can be fully replaced by a single learnable activation mechanism. We identify a plasticity suppression effect induced by standard normalization: learnable activation parameters rapidly lose adaptability when paired with normalization layers. Motivated by this observation, we introduce SALU (Saturated Adaptive Linear Unit), \\[ \\operatorname{SALU}(x;a,b) = \\frac{a x}{\\sqrt{1 + a b x^2}},\\quad a>0,\\; b>0 \\] a bounded, learnable activation "},"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":"2606.02927","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-01T22:09:06Z","cross_cats_sorted":[],"title_canon_sha256":"0c61b1ddd42321017ea1ffb43fea84ba9f54e71bc7f5f3450892df68854c2ff5","abstract_canon_sha256":"5000b6658a0e9b559ceb9812259acaded25d7108d701d82de7482de7b02f960e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:26.765662Z","signature_b64":"U0A8GZC3hBemNlj8uTOYKAWBZgKooh1WHO/fNL2WbFTxcNmc8oZwmKHEL6wSO7rxtAbjpHNzSg1r6jIymrRfCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5c6f01120db0e6f380799c15ef46f559870b9794e3a11f8c9f17b9d206cd43e9","last_reissued_at":"2026-06-03T01:05:26.765276Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:26.765276Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SaluNet: Enabling Total Plasticity in Normalization-Free Deep Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mourad Zaied (University of Gabes, Tuisia)","submitted_at":"2026-06-01T22:09:06Z","abstract_excerpt":"Normalization layers such as BatchNorm and LayerNorm have long been considered essential for stable training in deep networks. This work demonstrates that they can be fully replaced by a single learnable activation mechanism. We identify a plasticity suppression effect induced by standard normalization: learnable activation parameters rapidly lose adaptability when paired with normalization layers. Motivated by this observation, we introduce SALU (Saturated Adaptive Linear Unit), \\[ \\operatorname{SALU}(x;a,b) = \\frac{a x}{\\sqrt{1 + a b x^2}},\\quad a>0,\\; b>0 \\] a bounded, learnable activation "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.02927","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/2606.02927/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":"2606.02927","created_at":"2026-06-03T01:05:26.765330+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.02927v1","created_at":"2026-06-03T01:05:26.765330+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.02927","created_at":"2026-06-03T01:05:26.765330+00:00"},{"alias_kind":"pith_short_12","alias_value":"LRXQCEQNWDTP","created_at":"2026-06-03T01:05:26.765330+00:00"},{"alias_kind":"pith_short_16","alias_value":"LRXQCEQNWDTPHADZ","created_at":"2026-06-03T01:05:26.765330+00:00"},{"alias_kind":"pith_short_8","alias_value":"LRXQCEQN","created_at":"2026-06-03T01:05:26.765330+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/LRXQCEQNWDTPHADZTQK66RXVLG","json":"https://pith.science/pith/LRXQCEQNWDTPHADZTQK66RXVLG.json","graph_json":"https://pith.science/api/pith-number/LRXQCEQNWDTPHADZTQK66RXVLG/graph.json","events_json":"https://pith.science/api/pith-number/LRXQCEQNWDTPHADZTQK66RXVLG/events.json","paper":"https://pith.science/paper/LRXQCEQN"},"agent_actions":{"view_html":"https://pith.science/pith/LRXQCEQNWDTPHADZTQK66RXVLG","download_json":"https://pith.science/pith/LRXQCEQNWDTPHADZTQK66RXVLG.json","view_paper":"https://pith.science/paper/LRXQCEQN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.02927&json=true","fetch_graph":"https://pith.science/api/pith-number/LRXQCEQNWDTPHADZTQK66RXVLG/graph.json","fetch_events":"https://pith.science/api/pith-number/LRXQCEQNWDTPHADZTQK66RXVLG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LRXQCEQNWDTPHADZTQK66RXVLG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LRXQCEQNWDTPHADZTQK66RXVLG/action/storage_attestation","attest_author":"https://pith.science/pith/LRXQCEQNWDTPHADZTQK66RXVLG/action/author_attestation","sign_citation":"https://pith.science/pith/LRXQCEQNWDTPHADZTQK66RXVLG/action/citation_signature","submit_replication":"https://pith.science/pith/LRXQCEQNWDTPHADZTQK66RXVLG/action/replication_record"}},"created_at":"2026-06-03T01:05:26.765330+00:00","updated_at":"2026-06-03T01:05:26.765330+00:00"}