Dictionary Learning Based on Sparse Distribution Tomography
Authors: Pedram Pad, Farnood Salehi, Elisa Celis, Patrick Thiran, Michael Unser
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithm by performing two types of experiments: image inpainting and image denoising. In both cases, we find that our approach is competitive with stateof-the-art dictionary learning techniques. |
| Researcher Affiliation | Academia | 1Biomedical Imaging Group, EPFL, Lausanne, Switzerland 2Computer Communications and Applications Laboratory 3, EPFL, Lausanne, Switzerland. |
| Pseudocode | Yes | The pseudocode of our dictionary learning method is given in Algorithm 1. |
| Open Source Code | No | The paper mentions using a 'Python package SPAMS' and provides its URL, but does not state that the code for the method described in *this* paper is open-source or provide a link to their own implementation. |
| Open Datasets | Yes | We use a database of face images provided by AT&T4 and crop them to have size 112 91 so we can chop each image to 208 patches of size 7 7, which correspond to yi in our model. [4] www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html |
| Dataset Splits | No | The paper describes how data is used for training and testing but does not explicitly mention a dedicated validation set or specific train/validation/test split percentages/counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Python package SPAMS' but does not specify a version number for SPAMS or other software dependencies with their versions. |
| Experiment Setup | Yes | Algorithm 1 (Sparse DT) describes the initialization, iteration process, and adaptive step size parameters (η, κ+, κ-). It also mentions how the cost function E(B) is iteratively changed and how u vectors are regenerated randomly. |