Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Lightweight Image Super-Resolution via Flexible Meta Pruning
Authors: Yulun Zhang, Kai Zhang, Luc Van Gool, Martin Danelljan, Fisher Yu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to investigate critical factors in the flexible channel and weight pruning for image SR, demonstrating the superiority of our FMP when applied to baseline image SR architectures. |
| Researcher Affiliation | Academia | 1Mo E Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China 2School of Intelligence Science and Technology, Nanjing University, China 3ETH Z urich, Switzerland 4KU Leuven, Belgium 5INSAIT, Bulgaria. |
| Pseudocode | No | The paper describes the proposed method using text and mathematical equations, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Following most recent works (Timofte et al., 2017; Lim et al., 2017; Haris et al., 2018), we use DIV2K dataset (Timofte et al., 2017) and Flickr2K (Lim et al., 2017) as training data. |
| Dataset Splits | Yes | Following most recent works (Timofte et al., 2017; Lim et al., 2017; Haris et al., 2018), we use DIV2K dataset (Timofte et al., 2017) and Flickr2K (Lim et al., 2017) as training data. We use five standard benchmark datasets: Set5 (Bevilacqua et al., 2012), Set14 (Zeyde et al., 2010), B100 (Martin et al., 2001), Urban100 (Huang et al., 2015), and Manga109 (Matsui et al., 2017). ... We report inference time on Urban100 and DIV2K validation and test data. |
| Hardware Specification | Yes | We use Py Torch (Paszke et al., 2017) to implement our models with RTX 3090 GPUs. |
| Software Dependencies | No | We use Py Torch (Paszke et al., 2017) to implement our models with RTX 3090 GPUs. |
| Experiment Setup | Yes | We perform data augmentation on the training images, which are randomly rotated by 90 , 180 , 270 and flipped horizontally. Each training batch consists of 16 LR color patches, whose size is 64 64. Our FMP model is trained by ADAM optimizer (Kingma & Ba, 2015) 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. |