Projects per year
Abstract
Code for our publication "Designing Interpretable Recurrent Neural Networks for Video Reconstruction via Deep Unfolding". >> Code maintained on https://gitlab.com/etrovub/mlsp/reweighted-rnn << Citation: H. V. Luong, B. Joukovsky and N. Deligiannis, "Designing Interpretable Recurrent Neural Networks for Video Reconstruction via Deep Unfolding," in <em>IEEE Transactions on Image Processing</em>, vol. 30, pp. 4099-4113, 2021, doi: 10.1109/TIP.2021.3069296.
| Date made available | 2021 |
|---|---|
| Publisher | Zenodo |
| Date of data production | 2 Apr 2021 |
Keywords
- RNN
Format
- Format
- zip
Projects
- 2 Finished
-
FWOSB97: Interpretable and Explainable Deep Learning for Video Processing
Joukovsky, B. & Deligiannis, N.
1/11/20 → 31/10/24
Project: Fundamental
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SRP11: Strategic Research Programme: Processing of large scale multi-dimensional, multi-spectral, multi-sensorial and distributed data (M³D²)
Schelkens, P., Deligiannis, N., Jansen, B., Kuijk, M., Munteanu, A., Sahli, H., Steenhaut, K., Stiens, J., Schelkens, P., Cornelis, J. P., Kuijk, M., Munteanu, A., Sahli, H., Stiens, J. & Vounckx, R.
1/11/12 → 31/12/23
Project: Fundamental
Research output
- 1 Article
-
Designing Interpretable Recurrent Neural Networks for Video Reconstruction Via Deep Unfolding
Van Luong, H., Joukovsky, B. J. & Deligiannis, N., 2 Apr 2021, In: IEEE Transactions on Image Processing. 30, p. 4099 - 4113 15 p., 9394770.Research output: Contribution to journal › Article › peer-review
Open AccessFile18 Citations (Scopus)149 Downloads (Pure)