Sparse Coding

Reference sparse coding implementations for efficient learning and inference implemented in PyTorch with GPU support.

Dictionary Learning

• Repo currently includes classic patch-wise sparse coding dictionary learning.

Implemented Inference Methods

• Locally Competative Algorithm (LCA)
• Gradient Descent with Euler's method on Laplace Prior (Vanilla)
• Laplacian Scale Mixture (LSM)
• Iterative Shrinkage-threshold Algorithm (ISTA)
• Generic PyTorch minimization of arbitrary loss function (PyTorchOptimizer)

Setup

1. Clone the repo.
2. Navigate to the directory containing the repo directory.
3. Run pip install -e sparsecoding
4. Navigate into the repo and install the requirements using pip install -r requirements.txt
5. Install the natural images dataset from this link: https://rctn.org/bruno/sparsenet/IMAGES.mat
6. Try running the demo notebook: examples/sparse_coding.ipynb

Note: If you are using a Jupyter notebook and change a source file, you can either: 1) restart the Jupyter kernel, or 2) follow instructions here.

Contributing

See the contributing document!