{"url":"/method/hard-sigmoid","slug":"hard-sigmoid","name":"Hard Sigmoid","full_name":"Hard Sigmoid","full_name_withheld":false,"description_markdown":"The **Hard Sigmoid** is an activation function used for neural networks of the form:\r\n\r\n$$f\\left(x\\right) = \\max\\left(0, \\min\\left(1,\\frac{\\left(x+1\\right)}{2}\\right)\\right)$$\r\n\r\nImage Source: [Rinat Maksutov](https://towardsdatascience.com/deep-study-of-a-not-very-deep-neural-network-part-2-activation-functions-fd9bd8d406fc)","description_state":"present","introduced_year":null,"introduced_by":{"title":"BinaryConnect: Training Deep Neural Networks with binary weights during propagations","paper":"/paper/binaryconnect-training-deep-neural-networks","first_author":"Matthieu Courbariaux","n_authors":3,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/binaryconnect-training-deep-neural-networks"},"source":{"url":"http://arxiv.org/abs/1511.00363v3","title":"BinaryConnect: Training Deep Neural Networks with binary weights during propagations","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/tensorflow/tensorflow/blob/2b96f3662bd776e277f86997659e61046b56c315/tensorflow/python/keras/backend.py#L4716","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":4,"archive_num_papers":4,"papers_newest_first":[{"paper":null,"title":"K-textures, a self-supervised hard clustering deep learning algorithm for satellite image segmentation","date":"2022-05-18","arxiv_id":"2205.08671","n_code_links":0,"syntology":null},{"paper":null,"title":"MaskConvNet: Training Efficient ConvNets from Scratch via Budget-constrained Filter Pruning","date":"2020-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/l_0-arm-network-sparsification-via-stochastic","title":"$L_0$-ARM: Network Sparsification via Stochastic Binary Optimization","date":"2019-04-09","arxiv_id":"1904.04432","n_code_links":1,"syntology":null},{"paper":"/paper/binaryconnect-training-deep-neural-networks","title":"BinaryConnect: Training Deep Neural Networks with binary weights during propagations","date":"2015-11-02","arxiv_id":"1511.00363","n_code_links":5,"syntology":{"ran":3,"of":3,"unverified":0,"pointer_only":3}}],"papers_shown":4,"tasks":[{"task":"/task/clustering","name":"Clustering","papers":1},{"task":"/task/image-segmentation","name":"Image Segmentation","papers":1},{"task":"/task/network-pruning","name":"Network Pruning","papers":1},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":1}],"tasks_shown":4,"n_tasks":4,"usage_by_year":[{"year":"2015","papers":1},{"year":"2019","papers":1},{"year":"2020","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/hard-sigmoid"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}