Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors
Authors: VIET HO TAM THUC DO, Parham Eftekhar, Seyed Alireza Hosseini, Gene Cheung, Philip Chou
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments for two image interpolation applications verify the restoration performance, parameter efficiency and robustness to covariate shift of our graph-based unrolled networks compared to conventional transformers. (Abstract) |
| Researcher Affiliation | Collaboration | Tam Thuc Do York University Toronto, Canada dtamthuc@yorku.ca Parham Eftekhar York University Toronto, Canada eftekhar@yorku.ca Seyed Alireza Hosseini York University Toronto, Canada ahoseini@yorku.ca Gene Cheung York University Toronto, Canada genec@yorku.ca Philip A. Chou packet.media Seattle, USA pachou@ieee.org |
| Pseudocode | No | The paper describes iterative algorithms (CG, ADMM) through mathematical equations and textual explanations, but it does not provide a clearly labeled 'Pseudocode' or 'Algorithm' block with structured steps. |
| Open Source Code | No | The NeurIPS Paper Checklist for 'Open access to data and code' explicitly states 'No' and justifies it by saying, 'We used open source data: Mc M [43], Kodak [44], and Urban100 [45] datasets.', implying they did not release their own code. |
| Open Datasets | Yes | To train each learned model, we used the DIV2K dataset, which contains 800 and 100 high-resolution (HR) training and validation images, respectively. ... To test a model, we used the Mc M [43], Kodak [44], and Urban100 [45] datasets, running each model on the whole images. (Section 6.1) |
| Dataset Splits | Yes | Since the images are HR, we patchified the images into small images and used only about 1 to 4% of the patches for training and validation sets. We randomly sampled patches of 64 × 64 pixels to train the model. (Section 6.1) |
| Hardware Specification | Yes | All models were developed using Python 3.11. We leveraged Py Torch to implement all models and trained them using NVIDIA Ge Force RTX 2080 Ti. (Section 6.1) |
| Software Dependencies | Yes | All models were developed using Python 3.11. We leveraged Py Torch to implement all models and trained them using NVIDIA Ge Force RTX 2080 Ti. (Section 6.1) |
| Experiment Setup | Yes | A training dataset was created consisting of 5000, 10000, or 20000 image patches, each of size 64 × 64, to train the model. ... The parameters γ, α, β, and the metric matrix M were learned during training. ... In all ADMM blocks, we set the number of ADMM iterations to 5 and the number of CG iterations to 10. (Appendix A.8) |