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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting
Authors: Zongsheng Yue, Jianyi Wang, Chen Change Loy
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that the proposed method obtains superior or at least comparable performance to current state-of-the-art methods on both synthetic and real-world datasets, even only with 15 sampling steps. |
| Researcher Affiliation | Academia | Zongsheng Yue Jianyi Wang Chen Change Loy S-Lab, Nanyang Technological University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and model are available at https://github.com/zsy OAOA/Res Shift. |
| Open Datasets | Yes | HR images with a resolution of 256 256 in our training data are randomly cropped from the training set of Image Net [50] following LDM [11]. |
| Dataset Splits | No | HR images with a resolution of 256 256 in our training data are randomly cropped from the training set of Image Net [50] following LDM [11]. We synthesize a testing dataset that contains 3000 images randomly selected from the validation set of Image Net [50] based on the commonly-used degradation model, i.e., y = (x k) +n, where k is the blurring kernel, n is the noise, y and x denote the LR image and HR image, respectively. ... We name this dataset as Image Net-Test for convenience. |
| Hardware Specification | Yes | Running time is tested on NVIDIA Tesla V100 GPU on the x4 (64 256) SR task. |
| Software Dependencies | No | The Adam [51] algorithm with the default settings of Py Torch [52] and a mini-batch size of 64 is used to train Res Shift. (No specific PyTorch version is mentioned) |
| Experiment Setup | Yes | HR images with a resolution of 256 256 in our training data are randomly cropped from the training set of Image Net [50] following LDM [11]. We synthesize the LR images using the degradation pipeline of Real ESRGAN [19]. The Adam [51] algorithm with the default settings of Py Torch [52] and a mini-batch size of 64 is used to train Res Shift. During training, we use a fixed learning rate of 5e-5 and update the weight parameters for 500K iterations. As for the network architecture, we employ the UNet structure in DDPM [2]. To increase the robustness of Res Shift to arbitrary image resolution, we replace the self-attention layer in UNet with the Swin Transformer [53] block. |