Bayesian Dictionary Learning with Gaussian Processes and Sigmoid Belief Networks
Authors: Yizhe Zhang, Ricardo Henao, Chunyuan Li, Lawrence Carin
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Applications to image denoising, inpainting and depth-information restoration demonstrate that the proposed model outperforms other leading Bayesian dictionary learning approaches. We present experiments on two sets of images. The results on gray-scale images for denoising and inpainting tasks highlight how characterization of spatial structure improves results. |
| Researcher Affiliation | Academia | Duke University, Durham NC {yz196,rhenao,chunyuan.li,lcarin}@duke.edu |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide an unambiguous statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | We applied our methods to the 30 images of the Middlebury stereo dataset (Scharstein and Szeliski, 2002; Lu et al., 2014). |
| Dataset Splits | No | The paper describes MCMC iterations and sample collection for image reconstruction, but does not provide explicit train/validation/test dataset splits with percentages, sample counts, or predefined citations for data partitioning. |
| Hardware Specification | Yes | All the experiments were conducted on a single machine with two 2.7 GHz processors and 12 GB RAM. For each MCMC iteration, computations were parallelized w.r.t. dictionary elements using a desktop GPU. |
| Software Dependencies | No | The paper states 'code written in Matlab and C++', but does not provide specific version numbers for these or any other key software components or libraries. |
| Experiment Setup | Yes | The hyper-parameters controlling Gaussian distribution variances, i.e., σb and σλ, were all set to 0.1. As suggested in Zhou et al. (2009), the hyper-parameters for the inverse Gamma distributions (the priors for σw and σ") were set to {10^-6, 10^-6}. Dictionary sizes in both GPFA and GP-SBN-FA are initially set to 128. In GP-SBN-FA, we use a one-layer SBN with the number of top-layer binary units L set to half the size of the dictionary M. |