SSN: learning sparse switchable normalization via SparsestMax
type: Item
instance of: scholarly article
title: SSN: learning sparse switchable normalization via SparsestMax
zbMATH Open document ID: 1483.68346
author: Wenqi Shao
author: Jingyu Li
author: Jiamin Ren
author: Ruimao Zhang
author: Ping Luo
author: Xiao-Gang Wang
published in: International Journal of Computer Vision
publication date: 2021-12-01T00:00:00Z
full work available at URL: https://arxiv.org/abs/1903.03793
Mathematics Subject Classification ID: 68T07
zbMATH DE Number: 7437339
zbMATH Keywords: deep learning
zbMATH Keywords: normalization
zbMATH Keywords: classification
zbMATH Keywords: optimization
describes a project that uses: Inception-v4
describes a project that uses: ShuffleNet
describes a project that uses: Faster R-CNN
describes a project that uses: PyTorch
describes a project that uses: MS-COCO
describes a project that uses: SNAS
describes a project that uses: DeepLab
describes a project that uses: SPGL1
describes a project that uses: ArcFace
describes a project that uses: Detectron
describes a project that uses: DARTS
describes a project that uses: ADE20k
describes a project that uses: Cityscapes
describes a project that uses: MegaFace
describes a project that uses: GitHub
MaRDI profile type: Publication
OpenAlex ID: W3000218820
arXiv ID: 1903.03793
cites work: Fast projection onto the simplex and the \(l_1\) ball
cites work: Validation of subgradient optimization
cites work: Multilayer feedforward networks are universal approximators
cites work: Probing the Pareto Frontier for Basis Pursuit Solutions
DOI: 10.1007/S11263-019-01269-Y
description: scientific article