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-r
For datasets with low dimensionality and low difficulty level, traditional methods outperform sparse coding-based outlier detection (SCOD) algorithms. But the trend reverses as the dimensionality or difficulty level increases. A threshold emerges as the point of intersection of the trends for SCOD and...
4.1.3 Sparse coding methods Sparse coding proposed in Olshausen and Field (1997) provides a means to reduce sensor data dimension and represent them as an efficient linear combination of basis vectors. Due to the efficient data representation ability of sparse coding, a number of studies have use...
Edgar Roman-Rangel, Jean-Marc Odobez, Daniel Gatica- Perez, "Assessing Sparse Coding Methods for Contextual Shape Indexing of Maya Hieroglyphs", Journal of Multimedia, vol. 7, no. 2, pp. 179-192, 2012.Edgar Roman-Rangel, Jean-Marc Odobez, and Daniel Gatica-Perez. 2012. Assessing Sparse ...
we propose a novel sparse coding based method to investigate brain dynamics of freely-behaving mice from the perspective of functional connectivity, using super-long local field potential (LFP) recordings from 13 distinct regions of the mouse brain. Compared with surrogate datasets, six and four rep...
基于互补学习系统(Complementary Learning Systems,简称CLS)理论,提出了在DNNs中模仿大脑中不同记忆系统之间相互作用的想法。 本文提出了SCoMMER(Sparse Coding in a Multi-Memory Experience Replay mechanism)方法,其结合了稀疏编码和多记忆系统体验回放机制的方法,用于提高DNNs在持续学习任务中的表现。下图给出了SCoMMER...
H. Bristow and S. Lucey, "Optimization methods for convolutional sparse coding," arXiv preprint arXiv:1406.2407, 2014.BRISTOW H., LUCEY S.: Optimization Methods for Convolu- tional Sparse Coding. In arXiv:1406.2407 (2014). 9H. Bristow and S. Lucey. Optimization methods for convo- lution...
. The full sparse coding cost function hence is: where the constant is usually set One problem is that the constraint cannot be forced using simple gradient-based methods. Hence, in practice, this constraint is weakened to a "weight decay" term designed to keep the entries of ...
while sparse coding based methods mainly use hand-crafted features, which may not be optimal for video representation. For encoding-decoding neural networks based methods, 1) they merely concentrate on the (multi-) frame reconstruction errors in the pixel level, and ignore the high-level and sema...
We obtain broadly similar results with the Fashion-MNIST variant (Table 2): sparse coding improves linear decoding. However, this dataset is more challenging than the standard MNIST digits for non-convolutional algorithms, and the improvement is consequently smaller. Ensemble learning methods as well ...