{"url":"/method/halonet","slug":"halonet","name":"HaloNet","full_name":"HaloNet","full_name_withheld":false,"description_markdown":"A **HaloNet** is a self-attention based model for efficient image classification. It relies on a local self-attention architecture that efficiently maps to existing hardware with haloing. The formulation breaks translational equivariance, but the authors observe that it improves  throughput and accuracies over the centered local self-attention used in regular self-attention. The approach also utilises a strided self-attentive downsampling operation for multi-scale feature extraction.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Scaling Local Self-Attention for Parameter Efficient Visual Backbones","paper":"/paper/scaling-local-self-attention-for-parameter","first_author":"Ashish Vaswani","n_authors":6,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/scaling-local-self-attention-for-parameter"},"source":{"url":"https://arxiv.org/abs/2103.12731v3","title":"Scaling Local Self-Attention for Parameter Efficient Visual Backbones","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Image Models","url":"/methods/category/image-models","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":null,"title":"HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization","date":"2022-08-06","arxiv_id":"2208.03486","n_code_links":0,"syntology":null},{"paper":"/paper/scaling-local-self-attention-for-parameter","title":"Scaling Local Self-Attention for Parameter Efficient Visual Backbones","date":"2021-03-23","arxiv_id":"2103.12731","n_code_links":7,"syntology":{"ran":12,"of":20,"unverified":8,"pointer_only":0}}],"papers_shown":2,"tasks":[{"task":"/task/anomaly-detection","name":"Anomaly Detection","papers":1},{"task":"/task/image-classification","name":"Image Classification","papers":1},{"task":"/task/instance-segmentation","name":"Instance Segmentation","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/segmentation","name":"Segmentation","papers":1},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":1},{"task":"/task/transfer-learning","name":"Transfer Learning","papers":1},{"task":"/task/unsupervised-anomaly-detection","name":"Unsupervised Anomaly Detection","papers":1},{"task":"/task/object-detection-1","name":"object-detection","papers":1}],"tasks_shown":9,"n_tasks":9,"usage_by_year":[{"year":"2021","papers":1},{"year":"2022","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/halonet"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}