{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:ADWAIHYQTOVMFW63AFKRSBWJNC","short_pith_number":"pith:ADWAIHYQ","schema_version":"1.0","canonical_sha256":"00ec041f109baac2dbdb01551906c9689a8c8f5e3a64919ee9e621df684e55f2","source":{"kind":"arxiv","id":"1708.08311","version":1},"attestation_state":"computed","paper":{"title":"Deep Learning Sparse Ternary Projections for Compressed Sensing of Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Duc Minh Nguyen, Evaggelia Tsiligianni, Nikos Deligiannis","submitted_at":"2017-08-28T13:51:09Z","abstract_excerpt":"Compressed sensing (CS) is a sampling theory that allows reconstruction of sparse (or compressible) signals from an incomplete number of measurements, using of a sensing mechanism implemented by an appropriate projection matrix. The CS theory is based on random Gaussian projection matrices, which satisfy recovery guarantees with high probability; however, sparse ternary {0, -1, +1} projections are more suitable for hardware implementation. In this paper, we present a deep learning approach to obtain very sparse ternary projections for compressed sensing. Our deep learning architecture jointly "},"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":"1708.08311","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-28T13:51:09Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"321513a72152bbb78edbca6cb0e624c4a26cb1864d32a8ca4db5a704503f2f10","abstract_canon_sha256":"3475c7a30ee80e447cc031d8bd196252061b98d834ff0d6c96c17bcd26c4f1f2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:36:36.399189Z","signature_b64":"R+croUQTNFoft3ojPgmXdFB7smP9TmjJt+1Rs2P+UOJUBkvWS8b/CJdF4/kvAjLApu0I3D1lQDK07ahQadGxBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"00ec041f109baac2dbdb01551906c9689a8c8f5e3a64919ee9e621df684e55f2","last_reissued_at":"2026-05-18T00:36:36.398557Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:36:36.398557Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning Sparse Ternary Projections for Compressed Sensing of Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Duc Minh Nguyen, Evaggelia Tsiligianni, Nikos Deligiannis","submitted_at":"2017-08-28T13:51:09Z","abstract_excerpt":"Compressed sensing (CS) is a sampling theory that allows reconstruction of sparse (or compressible) signals from an incomplete number of measurements, using of a sensing mechanism implemented by an appropriate projection matrix. The CS theory is based on random Gaussian projection matrices, which satisfy recovery guarantees with high probability; however, sparse ternary {0, -1, +1} projections are more suitable for hardware implementation. In this paper, we present a deep learning approach to obtain very sparse ternary projections for compressed sensing. Our deep learning architecture jointly "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.08311","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":"1708.08311","created_at":"2026-05-18T00:36:36.398651+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.08311v1","created_at":"2026-05-18T00:36:36.398651+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.08311","created_at":"2026-05-18T00:36:36.398651+00:00"},{"alias_kind":"pith_short_12","alias_value":"ADWAIHYQTOVM","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_16","alias_value":"ADWAIHYQTOVMFW63","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_8","alias_value":"ADWAIHYQ","created_at":"2026-05-18T12:31:05.417338+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/ADWAIHYQTOVMFW63AFKRSBWJNC","json":"https://pith.science/pith/ADWAIHYQTOVMFW63AFKRSBWJNC.json","graph_json":"https://pith.science/api/pith-number/ADWAIHYQTOVMFW63AFKRSBWJNC/graph.json","events_json":"https://pith.science/api/pith-number/ADWAIHYQTOVMFW63AFKRSBWJNC/events.json","paper":"https://pith.science/paper/ADWAIHYQ"},"agent_actions":{"view_html":"https://pith.science/pith/ADWAIHYQTOVMFW63AFKRSBWJNC","download_json":"https://pith.science/pith/ADWAIHYQTOVMFW63AFKRSBWJNC.json","view_paper":"https://pith.science/paper/ADWAIHYQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.08311&json=true","fetch_graph":"https://pith.science/api/pith-number/ADWAIHYQTOVMFW63AFKRSBWJNC/graph.json","fetch_events":"https://pith.science/api/pith-number/ADWAIHYQTOVMFW63AFKRSBWJNC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ADWAIHYQTOVMFW63AFKRSBWJNC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ADWAIHYQTOVMFW63AFKRSBWJNC/action/storage_attestation","attest_author":"https://pith.science/pith/ADWAIHYQTOVMFW63AFKRSBWJNC/action/author_attestation","sign_citation":"https://pith.science/pith/ADWAIHYQTOVMFW63AFKRSBWJNC/action/citation_signature","submit_replication":"https://pith.science/pith/ADWAIHYQTOVMFW63AFKRSBWJNC/action/replication_record"}},"created_at":"2026-05-18T00:36:36.398651+00:00","updated_at":"2026-05-18T00:36:36.398651+00:00"}