Memory-Oriented Structural Pruning for Efficient Image Restoration
Authors: Xiangsheng Shi, Xuefei Ning, Lidong Guo, Tianchen Zhao, Enshu Liu, Yi Cai, Yuhan Dong, Huazhong Yang, Yu Wang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real image denoising, image super resolution and low-light image enhancement show that MOSP can yield models with higher memory efficiency while better preserving performance compared with baseline pruning methods. |
| Researcher Affiliation | Academia | 1 Department of Electronic Engineering, Tsinghua University 2 Shenzhen International Graduate School, Tsinghua University 3 School of Materials Science and Engineering, Tsinghua University |
| Pseudocode | Yes | To get a solution for the problem defined in Eqn. 1, we propose a memory-oriented pruning flow (see Appendix for detailed algorithm). |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | For real image denoising, we use 320 high-resolution images in the SIDD dataset (Abdelhamed, Lin, and Brown 2018) as the training data. |
| Dataset Splits | No | The paper mentions '320 high-resolution images... as the training data' and '1,280 validation patches in SIDD' for evaluation. However, it does not provide a complete training/test/validation split with explicit percentages or absolute counts for all three categories from a single dataset, nor does it explicitly define a 'test' set separate from 'validation'. |
| Hardware Specification | No | The paper does not specify any particular GPU models, CPU types, or other detailed hardware specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Adam optimizer' but does not provide specific version numbers for any software libraries (e.g., Python, PyTorch, TensorFlow) or other dependencies. |
| Experiment Setup | Yes | The models are trained on 256 256 patches with a batch size of 32. Random horizontal and vertical flips are applied to the training patches as data augmentation. We use Adam optimizer (Kingma and Ba 2014) with β1 = 0.9, β2 = 0.999, and ϵ = 1e 8. In the pretrain stage, we train the baseline models for 80 epochs, with the initial learning rate set as 1 10 4 and decreased to half every 20 epochs. ... The learning rate is set to 1 10 4 and decreased to 5 10 5 after 10 epochs. As for MOSP hyper-parameters, the outer memory stride is by default 2MB and the inner memory step is set to be the highest per-channel memory in the current selected group. |