{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:GHD7VUB3CBPBB54XKQLXBCFJZX","short_pith_number":"pith:GHD7VUB3","schema_version":"1.0","canonical_sha256":"31c7fad03b105e10f79754177088a9cdf8973a405e908f54c46a6aadda9dbed3","source":{"kind":"arxiv","id":"1705.04748","version":1},"attestation_state":"computed","paper":{"title":"Gabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.NE","authors_text":"Kaushik Roy, Priyadarshini Panda, Syed Shakib Sarwar","submitted_at":"2017-05-12T20:50:51Z","abstract_excerpt":"Convolutional Neural Networks (CNN) are being increasingly used in computer vision for a wide range of classification and recognition problems. However, training these large networks demands high computational time and energy requirements; hence, their energy-efficient implementation is of great interest. In this work, we reduce the training complexity of CNNs by replacing certain weight kernels of a CNN with Gabor filters. The convolutional layers use the Gabor filters as fixed weight kernels, which extracts intrinsic features, with regular trainable weight kernels. This combination creates a"},"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":"1705.04748","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-05-12T20:50:51Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"04886f8af9015d0753fadc4026d355e650ed3e5cd074b182edaf8bfa9afc9864","abstract_canon_sha256":"3b40f00417a63c3e960c3db312d2832918e20ea324bc0192f1075aaa67286339"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:55.015571Z","signature_b64":"lpWrDE/EZhppjGnjxybgjj7MaA5cbfrnb+oxSs05HQc6UFRt79317kwYMymhnWDz0/hMzW80OiAPYECkLmY8AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31c7fad03b105e10f79754177088a9cdf8973a405e908f54c46a6aadda9dbed3","last_reissued_at":"2026-05-18T00:30:55.014835Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:55.014835Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.NE","authors_text":"Kaushik Roy, Priyadarshini Panda, Syed Shakib Sarwar","submitted_at":"2017-05-12T20:50:51Z","abstract_excerpt":"Convolutional Neural Networks (CNN) are being increasingly used in computer vision for a wide range of classification and recognition problems. However, training these large networks demands high computational time and energy requirements; hence, their energy-efficient implementation is of great interest. In this work, we reduce the training complexity of CNNs by replacing certain weight kernels of a CNN with Gabor filters. The convolutional layers use the Gabor filters as fixed weight kernels, which extracts intrinsic features, with regular trainable weight kernels. This combination creates a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.04748","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":"1705.04748","created_at":"2026-05-18T00:30:55.014966+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.04748v1","created_at":"2026-05-18T00:30:55.014966+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.04748","created_at":"2026-05-18T00:30:55.014966+00:00"},{"alias_kind":"pith_short_12","alias_value":"GHD7VUB3CBPB","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_16","alias_value":"GHD7VUB3CBPBB54X","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_8","alias_value":"GHD7VUB3","created_at":"2026-05-18T12:31:18.294218+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/GHD7VUB3CBPBB54XKQLXBCFJZX","json":"https://pith.science/pith/GHD7VUB3CBPBB54XKQLXBCFJZX.json","graph_json":"https://pith.science/api/pith-number/GHD7VUB3CBPBB54XKQLXBCFJZX/graph.json","events_json":"https://pith.science/api/pith-number/GHD7VUB3CBPBB54XKQLXBCFJZX/events.json","paper":"https://pith.science/paper/GHD7VUB3"},"agent_actions":{"view_html":"https://pith.science/pith/GHD7VUB3CBPBB54XKQLXBCFJZX","download_json":"https://pith.science/pith/GHD7VUB3CBPBB54XKQLXBCFJZX.json","view_paper":"https://pith.science/paper/GHD7VUB3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.04748&json=true","fetch_graph":"https://pith.science/api/pith-number/GHD7VUB3CBPBB54XKQLXBCFJZX/graph.json","fetch_events":"https://pith.science/api/pith-number/GHD7VUB3CBPBB54XKQLXBCFJZX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GHD7VUB3CBPBB54XKQLXBCFJZX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GHD7VUB3CBPBB54XKQLXBCFJZX/action/storage_attestation","attest_author":"https://pith.science/pith/GHD7VUB3CBPBB54XKQLXBCFJZX/action/author_attestation","sign_citation":"https://pith.science/pith/GHD7VUB3CBPBB54XKQLXBCFJZX/action/citation_signature","submit_replication":"https://pith.science/pith/GHD7VUB3CBPBB54XKQLXBCFJZX/action/replication_record"}},"created_at":"2026-05-18T00:30:55.014966+00:00","updated_at":"2026-05-18T00:30:55.014966+00:00"}