{"url":"/method/dimconv","slug":"dimconv","name":"DimConv","full_name":"Dimension-wise Convolution","full_name_withheld":false,"description_markdown":"A **Dimension-wise Convolution**, or **DimConv**, is a type of [convolution](https://paperswithcode.com/method/convolution) that can encode depth-wise, width-wise, and height-wise information independently. To achieve this, DimConv extends depthwise convolutions to all dimensions of the input tensor $X \\in \\mathbb{R}^{D\\times{H}\\times{W}}$, where $W$, $H$, and $D$ corresponds to width, height, and depth of $X$. DimConv has three branches, one branch per dimension. These branches apply $D$ depth-wise convolutional kernels $k\\_{D} \\in \\mathbb{R}^{1\\times{n}\\times{n}}$ along depth, $W$ width-wise convolutional kernels $k\\_{W} \\in \\mathbb{R}^{n\\times{1}\\times{1}}$ along width, and $H$ height-wise convolutional kernels $k\\_{H} \\in \\mathbb{R}^{n\\times{1}\\times{n}}$ kernels along height\r\nto produce outputs $Y\\_{D}$, $Y\\_{W}$, and $Y\\_{H} \\in \\mathbb{R}^{D\\times{H}\\times{W}}$ that\r\nencode information from all dimensions of the input tensor. The outputs of these independent branches are concatenated along the depth dimension, such that the first spatial plane of $Y\\_{D}$, $Y\\_{W}$, and $Y\\_{H}$ are put together and so on, to produce the output $Y\\_{Dim} = ${$Y\\_{D}$, $Y\\_{W}$, $Y\\_{H}$} $\\in \\mathbb{R}^{3D\\times{H}\\times{W}}$.","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":"https://arxiv.org/abs/1906.03516v3","title":"DiCENet: Dimension-wise Convolutions for Efficient Networks","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/sacmehta/EdgeNets/blob/c4c515700df32bb0417de3dd1eb39cfe23bf9148/nn_layers/dice.py#L62","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Convolutions","url":"/methods/category/convolutions","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":null,"papers_newest_first":[{"paper":"/paper/dicenet-dimension-wise-convolutions-for","title":"DiCENet: Dimension-wise Convolutions for Efficient Networks","date":"2019-06-08","arxiv_id":"1906.03516","n_code_links":2,"syntology":{"ran":0,"of":1,"unverified":1,"pointer_only":0}}],"papers_shown":1,"tasks":[{"task":"/task/image-classification","name":"Image Classification","papers":1},{"task":"/task/architecture-search","name":"Neural Architecture Search","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/real-time-object-detection","name":"Real-Time Object Detection","papers":1},{"task":"/task/real-time-semantic-segmentation","name":"Real-Time Semantic Segmentation","papers":1},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":1},{"task":"/task/image-classification","name":"image-classification","papers":1},{"task":"/task/object-detection-1","name":"object-detection","papers":1}],"tasks_shown":8,"n_tasks":8,"usage_by_year":[{"year":"2019","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/dimconv"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}