{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:3PBQZAFR5LY4D33CQA7ZBNRAYB","short_pith_number":"pith:3PBQZAFR","schema_version":"1.0","canonical_sha256":"dbc30c80b1eaf1c1ef62803f90b620c071509bfcc27b24ed654bb448f74b2186","source":{"kind":"arxiv","id":"1606.02147","version":1},"attestation_state":"computed","paper":{"title":"ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abhishek Chaurasia, Adam Paszke, Eugenio Culurciello, Sangpil Kim","submitted_at":"2016-06-07T14:09:27Z","abstract_excerpt":"The ability to perform pixel-wise semantic segmentation in real-time is of paramount importance in mobile applications. Recent deep neural networks aimed at this task have the disadvantage of requiring a large number of floating point operations and have long run-times that hinder their usability. In this paper, we propose a novel deep neural network architecture named ENet (efficient neural network), created specifically for tasks requiring low latency operation. ENet is up to 18$\\times$ faster, requires 75$\\times$ less FLOPs, has 79$\\times$ less parameters, and provides similar or better acc"},"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":"1606.02147","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-07T14:09:27Z","cross_cats_sorted":[],"title_canon_sha256":"5fee59c626053c37db1d9fc29440b3b73081cc6ff6bf731940309449f025a33e","abstract_canon_sha256":"c6f6426c404f9940f5f4c54ecfef887d1d1de0824c0d34b3235a82fa35799cf3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:12:44.534858Z","signature_b64":"VMd4OAmJdUFTbn6XwH1xbBSdbFKSlShMKBCRhy+rb31vIwij00MCZxG+YLRTfvp5dDYu+oLdKAIXyl9M8FyFCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dbc30c80b1eaf1c1ef62803f90b620c071509bfcc27b24ed654bb448f74b2186","last_reissued_at":"2026-05-18T01:12:44.534485Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:12:44.534485Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abhishek Chaurasia, Adam Paszke, Eugenio Culurciello, Sangpil Kim","submitted_at":"2016-06-07T14:09:27Z","abstract_excerpt":"The ability to perform pixel-wise semantic segmentation in real-time is of paramount importance in mobile applications. Recent deep neural networks aimed at this task have the disadvantage of requiring a large number of floating point operations and have long run-times that hinder their usability. In this paper, we propose a novel deep neural network architecture named ENet (efficient neural network), created specifically for tasks requiring low latency operation. ENet is up to 18$\\times$ faster, requires 75$\\times$ less FLOPs, has 79$\\times$ less parameters, and provides similar or better acc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.02147","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":"1606.02147","created_at":"2026-05-18T01:12:44.534545+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.02147v1","created_at":"2026-05-18T01:12:44.534545+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.02147","created_at":"2026-05-18T01:12:44.534545+00:00"},{"alias_kind":"pith_short_12","alias_value":"3PBQZAFR5LY4","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_16","alias_value":"3PBQZAFR5LY4D33C","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_8","alias_value":"3PBQZAFR","created_at":"2026-05-18T12:29:55.572404+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":17,"internal_anchor_count":13,"sample":[{"citing_arxiv_id":"1906.09826","citing_title":"ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation","ref_index":17,"is_internal_anchor":true},{"citing_arxiv_id":"1906.11367","citing_title":"Accelerating Large-Kernel Convolution Using Summed-Area Tables","ref_index":22,"is_internal_anchor":true},{"citing_arxiv_id":"1907.01058","citing_title":"Associative Embedding for Game-Agnostic Team Discrimination","ref_index":25,"is_internal_anchor":true},{"citing_arxiv_id":"1907.06119","citing_title":"Understanding Deep Learning Techniques for Image Segmentation","ref_index":163,"is_internal_anchor":true},{"citing_arxiv_id":"1907.07772","citing_title":"Modern CNNs for IoT Based Farms","ref_index":37,"is_internal_anchor":true},{"citing_arxiv_id":"1907.07210","citing_title":"Real-time Vision-based Depth Reconstruction with NVidia Jetson","ref_index":23,"is_internal_anchor":true},{"citing_arxiv_id":"1907.11066","citing_title":"Importance-Aware Semantic Segmentation with Efficient Pyramidal Context Network for Navigational Assistant Systems","ref_index":13,"is_internal_anchor":true},{"citing_arxiv_id":"1907.11394","citing_title":"A Comparative Study of High-Recall Real-Time Semantic Segmentation Based on Swift Factorized Network","ref_index":16,"is_internal_anchor":true},{"citing_arxiv_id":"2301.01201","citing_title":"Uncertainty in Real-Time Semantic Segmentation on Embedded Systems","ref_index":18,"is_internal_anchor":true},{"citing_arxiv_id":"2402.02286","citing_title":"Attention-Mamba: A Mamba-Enhanced Multi-Scale Parallel Inference Network for Medical Image Segmentation","ref_index":63,"is_internal_anchor":true},{"citing_arxiv_id":"2403.16958","citing_title":"TwinLiteNet+: An Enhanced Multi-Task Segmentation Model for Autonomous Driving","ref_index":5,"is_internal_anchor":true},{"citing_arxiv_id":"2603.13941","citing_title":"Bidirectional Cross-Attention Fusion of High-Res RGB and Low-Res HSI for Multimodal Automated Waste Sorting","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"2603.17714","citing_title":"From Virtual Environments to Real-World Trials: Emerging Trends in Autonomous Driving","ref_index":28,"is_internal_anchor":true},{"citing_arxiv_id":"2605.09864","citing_title":"DA-SegFormer: Damage-Aware Semantic Segmentation for Fine-Grained Disaster Assessment","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08521","citing_title":"Geometric Flood Depth Estimation: Fusing Transformer-Based Segmentation with Digital Elevation Models","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13761","citing_title":"Design and Behavior of Sparse Mixture-of-Experts Layers in CNN-based Semantic Segmentation","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02764","citing_title":"FoR-Net: Learning to Focus on Hard Regions for Efficient Semantic Segmentation","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3PBQZAFR5LY4D33CQA7ZBNRAYB","json":"https://pith.science/pith/3PBQZAFR5LY4D33CQA7ZBNRAYB.json","graph_json":"https://pith.science/api/pith-number/3PBQZAFR5LY4D33CQA7ZBNRAYB/graph.json","events_json":"https://pith.science/api/pith-number/3PBQZAFR5LY4D33CQA7ZBNRAYB/events.json","paper":"https://pith.science/paper/3PBQZAFR"},"agent_actions":{"view_html":"https://pith.science/pith/3PBQZAFR5LY4D33CQA7ZBNRAYB","download_json":"https://pith.science/pith/3PBQZAFR5LY4D33CQA7ZBNRAYB.json","view_paper":"https://pith.science/paper/3PBQZAFR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.02147&json=true","fetch_graph":"https://pith.science/api/pith-number/3PBQZAFR5LY4D33CQA7ZBNRAYB/graph.json","fetch_events":"https://pith.science/api/pith-number/3PBQZAFR5LY4D33CQA7ZBNRAYB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3PBQZAFR5LY4D33CQA7ZBNRAYB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3PBQZAFR5LY4D33CQA7ZBNRAYB/action/storage_attestation","attest_author":"https://pith.science/pith/3PBQZAFR5LY4D33CQA7ZBNRAYB/action/author_attestation","sign_citation":"https://pith.science/pith/3PBQZAFR5LY4D33CQA7ZBNRAYB/action/citation_signature","submit_replication":"https://pith.science/pith/3PBQZAFR5LY4D33CQA7ZBNRAYB/action/replication_record"}},"created_at":"2026-05-18T01:12:44.534545+00:00","updated_at":"2026-05-18T01:12:44.534545+00:00"}