MatConvNet – convolutional neural networks for MATLAB. MatConvNet is an open source implementation of Convolutional Neural Networks (CNNs) with a deep integration in the MATLAB environment. The toolbox is designed with an emphasis on simplicity and flexibility. It exposes the building blocks of CNNs as easy-to-use MATLAB functions, providing routines for computing convolutions with filter banks, feature pooling, normalisation, and much more. MatConvNet can be easily extended, often using only MATLAB code, allowing fast prototyping of new CNN architectures. At the same time, it supports efficient computation on CPU and GPU, allowing to train complex models on large datasets such as ImageNet ILSVRC containing millions of training examples

References in zbMATH (referenced in 18 articles )

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  1. Xu, Tianyang; Feng, Zhenhua; Wu, Xiao-Jun; Kittler, Josef: Adaptive channel selection for robust visual object tracking with discriminative correlation filters (2021)
  2. Zhang, Jianjia; Wang, Lei; Zhou, Luping; Li, Wanqing: Beyond covariance: SICE and kernel based visual feature representation (2021)
  3. Hoiles, William; Krishnamurthy, Vikram; Pattanayak, Kunal: Rationally inattentive inverse reinforcement learning explains YouTube commenting behavior (2020)
  4. Tang, Xiwei; Bi, Xuan; Qu, Annie: Individualized multilayer tensor learning with an application in imaging analysis (2020)
  5. Tian, Chunwei; Fei, Lunke; Zheng, Wenxian; Xu, Yong; Zuo, Wangmeng; Lin, Chia-Wen: Deep learning on image denoising: an overview (2020)
  6. Higham, Catherine F.; Higham, Desmond J.: Deep learning: an introduction for applied mathematicians (2019)
  7. Hill, Mitch; Nijkamp, Erik; Zhu, Song-Chun: Building a telescope to look into high-dimensional image spaces (2019)
  8. Lenc, Karel; Vedaldi, Andrea: Understanding image representations by measuring their equivariance and equivalence (2019)
  9. Montobbio, Noemi; Citti, Giovanna; Sarti, Alessandro: From receptive profiles to a metric model of V1 (2019)
  10. Ahmad, Shahzor; Cheong, Loong-Fah: Robust detection and affine rectification of planar homogeneous texture for scene understanding (2018)
  11. Li, Hanhui; Wu, Hefeng; Lin, Shujin; Luo, Xiaonan: Coupling deep correlation filter and online discriminative learning for visual object tracking (2018)
  12. Raviv, Dolev; Hazan, Tamir; Osadchy, Margarita: Hinge-minimax learner for the ensemble of hyperplanes (2018)
  13. Wu, Meiyin; Chen, Li; Tian, Jing: A hybrid learning-based framework for blind image quality assessment (2018)
  14. Xie, Yuan; Tao, Dacheng; Zhang, Wensheng; Liu, Yan; Zhang, Lei; Qu, Yanyun: On unifying multi-view self-representations for clustering by tensor multi-rank minimization (2018)
  15. Ye, Jong Chul; Han, Yoseob; Cha, Eunju: Deep convolutional framelets: a general deep learning framework for inverse problems (2018)
  16. Fernando, Basura; Gould, Stephen: Discriminatively learned hierarchical rank pooling networks (2017)
  17. Wang, Qian; Chen, Ke: Zero-shot visual recognition via bidirectional latent embedding (2017)
  18. Ochs, Peter; Ranftl, René; Brox, Thomas; Pock, Thomas: Techniques for gradient-based bilevel optimization with non-smooth lower level problems (2016)