{"url":"/method/crelu","slug":"crelu","name":"CReLU","full_name":"CReLU","full_name_withheld":false,"description_markdown":"**CReLU**, or **Concatenated Rectified Linear Units**, is a type of activation function which preserves both positive and negative phase information while enforcing non-saturated non-linearity. We compute by concatenating the layer output $h$ as:\r\n\r\n$$ \\left[\\text{ReLU}\\left(h\\right), \\text{ReLU}\\left(-h\\right)\\right] $$","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"http://arxiv.org/abs/1603.05201v2","title":"Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units","url_on_a_paper_host":true},"code_snippet_url":"https://gist.github.com/lintangsutawika/f2f3fb422d6d7df28bd74e26940da2e6","code_snippet_url_on_a_code_host":true,"categories":[{"area":"General","area_id":"general","collection":"Activation Functions","url":"/methods/category/activation-functions","pwc_aliases":[]}],"n_papers_tagged":9,"archive_num_papers":null,"papers_newest_first":[{"paper":"/paper/diffusion-models-beat-gans-on-image","title":"Diffusion Models Beat GANs on Image Classification","date":"2023-07-17","arxiv_id":"2307.08702","n_code_links":1,"syntology":null},{"paper":null,"title":"Proper Straight-Through Estimator: Breaking symmetry promotes convergence to true minimum","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"title":"Neural Networks with Complex-Valued Weights Have No Spurious Local Minima","date":"2021-01-31","arxiv_id":"2103.07287","n_code_links":0,"syntology":null},{"paper":null,"title":"Complex neural networks have no spurious local minima","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/better-than-reference-in-low-light-image","title":"Better Than Reference In Low Light Image Enhancement: Conditional Re-Enhancement Networks","date":"2020-08-26","arxiv_id":"2008.11434","n_code_links":1,"syntology":null},{"paper":"/paper/big-gans-are-watching-you-towards","title":"Object Segmentation Without Labels with Large-Scale Generative Models","date":"2020-06-08","arxiv_id":"2006.04988","n_code_links":1,"syntology":null},{"paper":null,"title":"Reconstructing Natural Scenes from fMRI Patterns using BigBiGAN","date":"2020-01-31","arxiv_id":"2001.11761","n_code_links":0,"syntology":null},{"paper":"/paper/large-scale-adversarial-representation","title":"Large Scale Adversarial Representation Learning","date":"2019-07-04","arxiv_id":"1907.02544","n_code_links":4,"syntology":{"ran":0,"of":3,"unverified":3,"pointer_only":0}},{"paper":"/paper/understanding-and-improving-convolutional","title":"Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units","date":"2016-03-16","arxiv_id":"1603.05201","n_code_links":2,"syntology":null}],"papers_shown":9,"tasks":[{"task":"/task/image-classification","name":"Image Classification","papers":2},{"task":"/task/image-generation","name":"Image Generation","papers":2},{"task":"/task/image-classification","name":"image-classification","papers":2},{"task":"/task/attribute","name":"Attribute","papers":1},{"task":"/task/classification-1","name":"Classification","papers":1},{"task":"/task/contrastive-learning","name":"Contrastive Learning","papers":1},{"task":"/task/denoising","name":"Denoising","papers":1},{"task":"/task/fine-grained-image-classification","name":"Fine-Grained Image Classification","papers":1},{"task":null,"name":"GPU","papers":1},{"task":null,"name":"Generative Adversarial Network","papers":1},{"task":"/task/image-enhancement","name":"Image Enhancement","papers":1},{"task":"/task/image-reconstruction","name":"Image Reconstruction","papers":1},{"task":"/task/low-light-image-enhancement","name":"Low-Light Image Enhancement","papers":1},{"task":"/task/object","name":"Object","papers":1},{"task":"/task/representation-learning","name":"Representation Learning","papers":1},{"task":"/task/saliency-detection","name":"Saliency Detection","papers":1},{"task":"/task/segmentation","name":"Segmentation","papers":1},{"task":"/task/self-supervised-image-classification","name":"Self-Supervised Image Classification","papers":1},{"task":"/task/self-supervised-learning","name":"Self-Supervised Learning","papers":1},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":1}],"tasks_shown":20,"n_tasks":26,"usage_by_year":[{"year":"2016","papers":1},{"year":"2019","papers":1},{"year":"2020","papers":3},{"year":"2021","papers":3},{"year":"2023","papers":1}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/crelu"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}