{"url":"/method/simplenet","slug":"simplenet","name":"SimpleNet","full_name":"SimpleNet","full_name_withheld":false,"description_markdown":"**SimpleNet** is a convolutional neural network with 13 layers. The network employs a homogeneous design utilizing 3 × 3 kernels for convolutional layer and 2 × 2 kernels for pooling operations. The only layers which do not use 3 × 3 kernels are 11th and 12th layers, these layers, utilize 1 × 1 convolutional kernels. Feature-map down-sampling is carried out using nonoverlaping 2 × 2 max-pooling. In order to cope with the problem of vanishing gradient and also over-fitting, SimpleNet also uses batch-normalization with moving average fraction of 0.95 before any [ReLU](https://paperswithcode.com/method/relu) non-linearity.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures","paper":"/paper/lets-keep-it-simple-using-simple","first_author":"Seyyed Hossein Hasanpour","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/lets-keep-it-simple-using-simple"},"source":{"url":"https://arxiv.org/abs/1608.06037v8","title":"Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/Coderx7/SimpleNet_Pytorch/blob/5d13ddbba6ae531ced26469c6b0f0ec18665d5ec/models/simplenet.py#L12","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":6,"archive_num_papers":6,"papers_newest_first":[{"paper":"/paper/simplenet-a-simple-network-for-image-anomaly","title":"SimpleNet: A Simple Network for Image Anomaly Detection and Localization","date":"2023-03-27","arxiv_id":"2303.15140","n_code_links":2,"syntology":{"ran":10,"of":17,"unverified":7,"pointer_only":0}},{"paper":null,"title":"EIS -- a family of activation functions combining Exponential, ISRU, and Softplus","date":"2020-09-28","arxiv_id":"2009.13501","n_code_links":0,"syntology":null},{"paper":null,"title":"TanhSoft -- a family of activation functions combining Tanh and Softplus","date":"2020-09-08","arxiv_id":"2009.03863","n_code_links":0,"syntology":null},{"paper":"/paper/tidying-deep-saliency-prediction","title":"Tidying Deep Saliency Prediction Architectures","date":"2020-03-10","arxiv_id":"2003.04942","n_code_links":1,"syntology":{"ran":0,"of":5,"unverified":5,"pointer_only":0}},{"paper":"/paper/mish-a-self-regularized-non-monotonic-neural","title":"Mish: A Self Regularized Non-Monotonic Activation Function","date":"2019-08-23","arxiv_id":"1908.08681","n_code_links":9,"syntology":{"ran":3,"of":12,"unverified":9,"pointer_only":0}},{"paper":"/paper/lets-keep-it-simple-using-simple","title":"Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures","date":"2016-08-22","arxiv_id":"1608.06037","n_code_links":9,"syntology":{"ran":3,"of":11,"unverified":8,"pointer_only":0}}],"papers_shown":6,"tasks":[{"task":"/task/image-classification","name":"Image Classification","papers":3},{"task":"/task/anomaly-classification","name":"Anomaly Classification","papers":1},{"task":"/task/anomaly-detection","name":"Anomaly Detection","papers":1},{"task":"/task/anomaly-segmentation","name":"Anomaly Segmentation","papers":1},{"task":"/task/decoder","name":"Decoder","papers":1},{"task":null,"name":"GPU","papers":1},{"task":"/task/classification","name":"General Classification","papers":1},{"task":"/task/novelty-detection","name":"Novelty Detection","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/prediction","name":"Prediction","papers":1},{"task":"/task/saliency-prediction","name":"Saliency Prediction","papers":1},{"task":"/task/object-detection-1","name":"object-detection","papers":1}],"tasks_shown":12,"n_tasks":12,"usage_by_year":[{"year":"2016","papers":1},{"year":"2019","papers":1},{"year":"2020","papers":3},{"year":"2023","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/simplenet"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}