Aligned Structured Sparsity Learning for Efficient Image Super-Resolution
Authors: Yulun Zhang, Huan Wang, Can Qin, Yun Fu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive comparisons with lightweight SR networks. Our ASSLN achieves superior performance gains over recent methods quantitatively and visually. [...] 4 Experimental Results |
| Researcher Affiliation | Academia | 1Department of ECE, Northeastern University 2Khoury College of Computer Science, Northeastern University |
| Pseudocode | No | The paper states, 'We provide the detailed algorithm in the supplementary material.', implying it is not in the main text. |
| Open Source Code | Yes | Our code and trained models are available at https://github.com/MingSun-Tse/ASSL. |
| Open Datasets | Yes | Following most recent works [56, 38, 63, 19], we use DIV2K [56] and Flickr2K [38] as training data. |
| Dataset Splits | No | The paper mentions training and testing datasets ('we use DIV2K [56] and Flickr2K [38] as training data' and 'For testing, we use five standard benchmark datasets: Set5 [2], Set14 [62], B100 [43], Urban100 [26], and Manga109 [44]'), but does not explicitly provide details about a separate validation split or how the data is partitioned for validation. |
| Hardware Specification | Yes | We use Py Torch [49] to implement our models with a Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch [49]' but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | Each training batch consists of 16 LR color patches, whose size is 48 48. Our ASSLN model is trained by ADAM optimizer [32] with β1=0.9, β2=0.999, and ϵ=10 8. We set the initial learning rate as 10 4 and then decrease it to half every 2 105 iterations. |