Name
Abstract | ||
|---|---|---|
Separable, or Kronecker product, dictionaries provide natural decompositions for 2D signals, such as images. In this paper, we describe a highly parallelizable algorithm that learns such dictionaries which reaches sparse representations competitive with the previous state of the art dictionary learning algorithms from the literature but at a lower computational cost. We highlight the performance o... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICPADS53394.2021.00053 | 2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS) |
Keywords | DocType | ISBN |
Training,Dictionaries,Conferences,Machine learning,Computational efficiency,Distributed algorithms,Image denoising | Conference | 978-1-6654-0878-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cristian Rusu | 1 | 399 | 45.44 |
| Paul Irofti | 2 | 4 | 2.90 |