Rethinking Conditional Diffusion Sampling with Progressive Guidance
Authors: Anh-Dung Dinh, Daochang Liu, Chang Xu
NeurIPS 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments", "Table 1: Pro G helps to achieve better IS/FID/s FID in general.", "Extensive experiments are conducted on CIFAR10, Image Net (64x64, 128x128, 256x256). |
| Researcher Affiliation | Academia | Anh-Dung Dinh School of Computer Science The University of Sydney EMAIL", "Daochang Liu School of Computer Science The University of Sydney EMAIL", "Chang Xu School of Computer Science The University of Sydney EMAIL |
| Pseudocode | No | The paper does not include a dedicated section or figure explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Source code is available at: https://github.com/dungdinhanh/prog-guided-diffusion. |
| Open Datasets | Yes | Extensive experiments are conducted on CIFAR10, Image Net (64x64, 128x128, 256x256). |
| Dataset Splits | No | The paper mentions the use of datasets like ImageNet and CIFAR10, but it does not explicitly provide specific details on the train/validation/test dataset splits (e.g., percentages or sample counts) used for its own experiments. |
| Hardware Specification | No | The paper mentions that 'The AI training platform supporting this work was provided by High-Flyer AI (Hangzhou High-Flyer AI Fundamental Research Co., Ltd.)' but does not specify any particular hardware components like GPU or CPU models. |
| Software Dependencies | No | The paper references various models and frameworks (e.g., ADM, IDDPM, CLIP) but does not provide specific software dependencies with version numbers (e.g., Python version, library versions) for reproducibility. |
| Experiment Setup | Yes | Setup. Extensive experiments are conducted on CIFAR10, Image Net (64x64, 128x128, 256x256). We denote Progressive Guidance (Pro G) as our proposed method, which is first evaluated on ADM [11] and IDDPM [3] to verify our claims on improving the performance of the vanilla guidance method." and "Table 5: γ sensitivity comparision." and "When increasing the guidance scale, our proposed method mostly has a slower degeneration rate in FID and Recall than the vanilla guidance. |