Image denoising with sparse coding through K-SVD
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Updated
Jan 9, 2024 - Python
Image denoising with sparse coding through K-SVD
A scikit-learn based implementation of Sparse-Net.
Implementations from "A New Basis for Sparse PCA" paper at https://arxiv.org/abs/2007.00596
This is the algorithmic implementation of orthogonal sparse coding algorithm used in sparse coding applications
HTM and sparse representation of MNIST
Iterative winners-take-all algorithm
The project is about text sunmmarization with sparse coding
Chinese Historical Phonology
Early stages of incorporating self-supervised with algorithm unrolling. Code was written as part of a master's thesis (60 ECTS) at Aalborg University, Denmark.
Hierarchical sparse coding using greedy matching pursuit.
Discriminative Dictionary Learning for (2D) Image Segmentation
PyTorch implementation, with CUDA support, of the sparse coding algorithm based on the paper by Olshausen and Field (1997).
Master's thesis about sparse approximation and dictionary learning using Cloud K-SVD for image denoising. Results show that the algorithm is able to learn sparse representations of signal vectors from distributed data samples in a heterogeneous network setup.
Transform-Invariant Non-Negative Matrix Factorization
This repo for the paper titled "SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification"
Sparse coding in PyTorch via the Locally Competitive Algorithm (LCA)
An unsupervised compressed-sensing technique for fundamental objects selection
Sparse representation solvers for P0- and P1-problems
Python Implementation of Proximal Methods for Hierarchical Sparse Coding
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