Sparse codingVariational autoencoderLearned iterative shrinkage thresholding algorithmLearning rich data representations from unlabeled data is a key challenge towards applying deep learning algorithms in downstream tasks. Several variants of variational autoencoders (VAEs) have been proposed to learn compact ...
Yes, sparse-coding-based methods are still important in the field of super-resolution, although their significance has evolved over time. Sparse coding has been a crucial component in the development of super-resolution algorithms, and it provided a solid foundation for later methods. It essentially...
method based on sparsely coded motion attention for de- tecting both GAE and LAE in crowded scenes. In this work, we apply the properties of sparse coding in measuring the uniqueness of events from the perspective of motion atten- tion. Fig. 1 illustrates the overview of the proposed method...
The sparse coding based linear spatial pyramid matching (ScSPM) is a popular SC-embedded classification method [9]. It computes a spatial pyramid image representation with SC of local descriptors instead of the K-means vector quantization (VQ) in traditional SPM [9], [10], and thus ...
The convolutional sparse coding-based super-resolution (CSC-SR) method has shown its good performance in single image super-resolution. It divides the low-resolution (LR) image into low-frequency part and the high-frequency part, and reconstructs their corresponding high-resolution (HR) image ...
Experimental results show the sparse coding-based deep learning algorithm achieves higher accuracy for structural health monitoring under the same level of environmental noises, compared with some existing methods. 展开 关键词: Structural health monitoring Sparse coding Wireless sensor network ...
A novel sparse coding based wideband spectrum sensing technique for multiple frequency hopping spread spectrum primary users has been proposed. The proposed method relies on the sparsity of the received target signal on a learned exemplar dictionary. The learned exemplar dictionary is derived from traini...
面向生物信息感知网络稀疏脑电测量的模糊粗糙情绪识别 fuzzy rough emotion recognition based on sparse eeg sensing in biosensor network 热度: learning fast approximations of sparse coding:稀疏编码学习的快速逼近 热度: a general framework for image fusion based on multi-scale transform and sparse ...
MambaReg: Mamba-Based Disentangled Convolutional Sparse Coding for Unsupervised Deformable Multi-Modal Image Registration 3 Nov 2024 · Kaiang Wen, Bin Xie, Bin Duan, Yan Yan · Edit social preview Precise alignment of multi-modal images with inherent feature discrepancies poses a pivotal challenge ...
In the current decade, scalability has been developed in video coding (VC) schemes to reply end-user demands and heterogeneity of networks. In this paper, a low bit-rate signal-to-noise ratio (SNR) scalable VC based on dictionary learning (DL) and sparse representation is proposed. A notabl...