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].
Accelerating Diffusion Models for Inverse Problems through Shortcut Sampling
Authors: Gongye Liu, Haoze Sun, Jiayi Li, Fei Yin, Yujiu Yang
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we demonstrate SSD s effectiveness on multiple representative IR tasks. Our method achieves competitive results with only 30 NFEs compared to state-of-theart zero-shot methods(100 NFEs) and outperforms them with 100 NFEs in certain tasks. |
| Researcher Affiliation | Academia | Gongye Liu , Haoze Sun , Jiayi Li , Fei Yin , Yujiu Yang Tsinghua University EMAIL, EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Code is available at https://github.com/Gongye Liu/SSD. |
| Open Datasets | Yes | To evaluate the performance of SSD, we conduct experiments on two datasets with different distribution characters: Celeb A 256 256 [Karras et al., 2017] for face images and Image Net 256 256 [Deng et al., 2009] for natural images, both containing 1k validation images independent of the training dataset. |
| Dataset Splits | Yes | To evaluate the performance of SSD, we conduct experiments on two datasets with different distribution characters: Celeb A 256 256 [Karras et al., 2017] for face images and Image Net 256 256 [Deng et al., 2009] for natural images, both containing 1k validation images independent of the training dataset. |
| Hardware Specification | Yes | All of our experiments are conducted on a single NVIDIA RTX 2080Ti GPU. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python, PyTorch versions) were mentioned in the paper. |
| Experiment Setup | No | The paper describes general experimental settings like datasets and evaluation metrics, but does not provide specific hyperparameters such as learning rate, batch size, or optimizer settings. |