{"url":"/method/regnety","slug":"regnety","name":"RegNetY","full_name":"RegNetY","full_name_withheld":false,"description_markdown":"**RegNetY** is a convolutional network design space with simple, regular models with parameters: depth $d$, initial width $w\\_{0} > 0$, and slope $w\\_{a} > 0$, and generates a different block width $u\\_{j}$ for each block $j < d$. The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):\r\n\r\n$$ u\\_{j} = w\\_{0} + w\\_{a}\\cdot{j} $$\r\n\r\nFor **RegNetX** we have additional restrictions: we set $b = 1$ (the bottleneck ratio), $12 \\leq d \\leq 28$, and $w\\_{m} \\geq 2$ (the width multiplier).\r\n\r\nFor **RegNetY** we make one change, which is to include Squeeze-and-Excitation blocks.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Designing Network Design Spaces","paper":"/paper/designing-network-design-spaces","first_author":"Ilija Radosavovic","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/designing-network-design-spaces"},"source":{"url":"https://arxiv.org/abs/2003.13678v1","title":"Designing Network Design Spaces","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/facebookresearch/pycls/blob/ecfb53186b426002020f1a580c3d7d7ad723e283/pycls/models/regnet.py#L50","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Convolutional Neural Networks","url":"/methods/category/convolutional-neural-networks","pwc_aliases":[]}],"n_papers_tagged":24,"archive_num_papers":24,"papers_newest_first":[{"paper":null,"title":"Analyzing Breast Cancer Survival Disparities by Race and Demographic Location: A Survival Analysis Approach","date":"2025-06-08","arxiv_id":"2506.07191","n_code_links":0,"syntology":null},{"paper":null,"title":"Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare","date":"2025-05-22","arxiv_id":"2505.16190","n_code_links":0,"syntology":null},{"paper":"/paper/tackling-small-sample-survival-analysis-via","title":"Tackling Small Sample Survival Analysis via Transfer Learning: A Study of Colorectal Cancer Prognosis","date":"2025-01-21","arxiv_id":"2501.12421","n_code_links":1,"syntology":null},{"paper":"/paper/prediction-of-lung-metastasis-from","title":"Prediction of Lung Metastasis from Hepatocellular Carcinoma using the SEER Database","date":"2025-01-20","arxiv_id":"2501.11720","n_code_links":1,"syntology":null},{"paper":null,"title":"SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented Generation","date":"2024-10-15","arxiv_id":"2410.11315","n_code_links":0,"syntology":null},{"paper":null,"title":"Efficient Preference-based Reinforcement Learning via Aligned Experience Estimation","date":"2024-05-29","arxiv_id":"2405.18688","n_code_links":0,"syntology":null},{"paper":"/paper/seer-facilitating-structured-reasoning-and","title":"SEER: Facilitating Structured Reasoning and Explanation via Reinforcement Learning","date":"2024-01-24","arxiv_id":"2401.13246","n_code_links":1,"syntology":{"ran":13,"of":16,"unverified":3,"pointer_only":1}},{"paper":null,"title":"Survival Analysis of Young Triple-Negative Breast Cancer Patients","date":"2024-01-15","arxiv_id":"2401.08712","n_code_links":0,"syntology":null},{"paper":null,"title":"Adversarial Attacks on Image Classification Models: Analysis and Defense","date":"2023-12-28","arxiv_id":"2312.16880","n_code_links":0,"syntology":null},{"paper":"/paper/seer-a-knapsack-approach-to-exemplar","title":"SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA","date":"2023-10-10","arxiv_id":"2310.06675","n_code_links":1,"syntology":{"ran":6,"of":16,"unverified":10,"pointer_only":0}},{"paper":null,"title":"SEER: Super-Optimization Explorer for HLS using E-graph Rewriting with MLIR","date":"2023-08-15","arxiv_id":"2308.07654","n_code_links":0,"syntology":null},{"paper":"/paper/hiding-in-plain-sight-disguising-data","title":"Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning","date":"2023-06-05","arxiv_id":"2306.03013","n_code_links":2,"syntology":null},{"paper":"/paper/perfect-is-the-enemy-of-test-oracle","title":"Perfect is the enemy of test oracle","date":"2023-02-03","arxiv_id":"2302.01488","n_code_links":1,"syntology":null},{"paper":"/paper/auton-survival-an-open-source-package-for","title":"auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data","date":"2022-04-15","arxiv_id":"2204.07276","n_code_links":3,"syntology":null},{"paper":null,"title":"Influence of different factors on survival of patients with colorectal cancer","date":"2022-02-05","arxiv_id":"2202.03425","n_code_links":0,"syntology":null},{"paper":"/paper/survtrace-transformers-for-survival-analysis","title":"SurvTRACE: Transformers for Survival Analysis with Competing Events","date":"2021-10-02","arxiv_id":"2110.00855","n_code_links":1,"syntology":{"ran":1,"of":9,"unverified":8,"pointer_only":0}},{"paper":"/paper/encoder-decoder-architectures-for-clinically","title":"Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation","date":"2021-06-21","arxiv_id":"2106.11447","n_code_links":1,"syntology":null},{"paper":null,"title":"Prediction of Prognosis and Survival of Patients with Gastric Cancer by Weighted Improved Random Forest Model","date":"2021-04-10","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/efficient-visual-pretraining-with-contrastive","title":"Efficient Visual Pretraining with Contrastive Detection","date":"2021-03-19","arxiv_id":"2103.10957","n_code_links":2,"syntology":null},{"paper":"/paper/improving-computational-efficiency-in-visual","title":"Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings","date":"2021-03-04","arxiv_id":"2103.02886","n_code_links":1,"syntology":null},{"paper":"/paper/self-supervised-pretraining-of-visual","title":"Self-supervised Pretraining of Visual Features in the Wild","date":"2021-03-02","arxiv_id":"2103.01988","n_code_links":1,"syntology":null},{"paper":null,"title":"MobileDepth: Efficient Monocular Depth Prediction on Mobile Devices","date":"2020-11-20","arxiv_id":"2011.10189","n_code_links":0,"syntology":null},{"paper":"/paper/the-dongniao-international-birds-10000","title":"The DongNiao International Birds 10000 Dataset","date":"2020-09-21","arxiv_id":"2010.06454","n_code_links":0,"syntology":null},{"paper":"/paper/designing-network-design-spaces","title":"Designing Network Design Spaces","date":"2020-03-30","arxiv_id":"2003.13678","n_code_links":26,"syntology":{"ran":11,"of":53,"unverified":42,"pointer_only":0}}],"papers_shown":24,"tasks":[{"task":"/task/survival-analysis","name":"Survival Analysis","papers":5},{"task":"/task/image-classification","name":"Image Classification","papers":4},{"task":"/task/prognosis","name":"Prognosis","papers":4},{"task":"/task/decoder","name":"Decoder","papers":3},{"task":"/task/reinforcement-learning","name":"Reinforcement Learning","papers":3},{"task":"/task/transfer-learning","name":"Transfer Learning","papers":3},{"task":"/task/reinforcement-learning-2","name":"reinforcement-learning","papers":3},{"task":"/task/machine-learning","name":"BIG-bench Machine Learning","papers":2},{"task":"/task/epidemiology","name":"Epidemiology","papers":2},{"task":"/task/federated-learning","name":"Federated Learning","papers":2},{"task":"/task/question-answering","name":"Question Answering","papers":2},{"task":"/task/reinforcement-learning-1","name":"Reinforcement Learning (RL)","papers":2},{"task":"/task/adversarial-attack","name":"Adversarial Attack","papers":1},{"task":"/task/atari-games","name":"Atari Games","papers":1},{"task":"/task/chunking","name":"Chunking","papers":1},{"task":"/task/classification-1","name":"Classification","papers":1},{"task":"/task/computational-efficiency","name":"Computational Efficiency","papers":1},{"task":"/task/coronary-artery-segmentation","name":"Coronary Artery Segmentation","papers":1},{"task":"/task/data-augmentation","name":"Data Augmentation","papers":1},{"task":"/task/deep-reinforcement-learning","name":"Deep Reinforcement Learning","papers":1}],"tasks_shown":20,"n_tasks":49,"usage_by_year":[{"year":"2020","papers":3},{"year":"2021","papers":6},{"year":"2022","papers":2},{"year":"2023","papers":5},{"year":"2024","papers":4},{"year":"2025","papers":4}],"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/regnety"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}