Knowledge Distillation based Degradation Estimation for Blind Super-Resolution
Authors: Bin Xia, Yulun Zhang, Yitong Wang, Yapeng Tian, Wenming Yang, Radu Timofte, Luc Van Gool
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments under classic and real-world degradation settings. The results show that KDSR achieves SOTA performance and can generalize to various degradation processes. |
| Researcher Affiliation | Collaboration | Bin Xia1, Yulun Zhang2, Yitong Wang3, Yapeng Tian4, Wenming Yang1 , Radu Timofte5, and Luc Van Gool2 1Tsinghua University 2ETH Z urich 3Byte Dance Inc 4University of Texas at Dallas 5University of W urzburg |
| Pseudocode | No | The information is insufficient as the paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at Github. |
| Open Datasets | Yes | 800 images in DIV2K (Agustsson & Timofte, 2017) and 2,650 images in Flickr2K (Timofte et al., 2017) as the DF2K training set. |
| Dataset Splits | Yes | Since AIM19 and NTIRE2020 datasets provide a paired validation set, we use the LPIPS (Zhang et al., 2018b), PSNR, and SSIM for the evaluation. |
| Hardware Specification | No | The information is insufficient as the paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The information is insufficient as the paper does not provide specific ancillary software details (e.g., library names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The batch sizes are set to 64, and the LR patch sizes are 64 64. We use Adam optimizer with β1 = 0.9, β2 = 0.99. We train both teacher and student networks with 600 epochs and set their initial learning rate to 10 4 and decrease to half after every 150 epochs. The loss coefficient λrec and λkd are set to 1 and 0.15 separately. For optimization, we use Adam with β1 = 0.9, β2 = 0.99. In both two stages of training, we set the batch size to 48, with the input patch size being 64. |