Don't Play Favorites: Minority Guidance for Diffusion Models
Authors: Soobin Um, Suhyeon Lee, Jong Chul Ye
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on benchmark real datasets demonstrate that our minority guidance can greatly improve the capability of generating high-quality minority samples over existing generative samplers. We showcase that the performance benefit of our framework persists even in demanding real-world scenarios such as medical imaging, further underscoring the practical significance of our work. |
| Researcher Affiliation | Academia | Soobin Um, Suhyeon Lee & Jong Chul Ye KAIST, Daejeon, Republic of Korea {sum,suhyeon.lee,jong.ye}@kaist.ac.kr |
| Pseudocode | No | The paper describes its methods through prose and mathematical equations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/soobin-um/minority-guidance. |
| Open Datasets | Yes | Datasets. Our experiments are conducted on six real benchmarks: four unconditional and two class-conditional datasets. For the unconditional settings, we employ Celeb A 64^2 (Liu et al., 2015), CIFAR-10 (Krizhevsky et al., 2009), and LSUN-Bedrooms 256^2 (Yu et al., 2015)... We use Image Net 64^2 and 256^2 (Deng et al., 2009)... |
| Dataset Splits | No | The paper mentions using specific subsets of real data for evaluating generated samples (e.g., "10K and 5K real samples yielding the highest Avgk NN values for Celeb A and CIFAR-10"), but these are for evaluation metrics, not explicit validation dataset splits for model training or tuning. There is no explicit description of a validation split for the main models used. |
| Hardware Specification | Yes | All these results were obtained using a single A100 GPU. |
| Software Dependencies | No | Our implementation is based on Py Torch (Paszke et al., 2019). While PyTorch is mentioned, a specific version number is not provided, nor are explicit versions for other libraries or dependencies. |
| Experiment Setup | Yes | For the number of minority classes L, we take L = 100 for the three unconditional natural image datasets and L = 25 for Image Net and CIFAR-10-LT. We use L = 50 for the brain MRI experiments... We swept over {1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0, 6.0, . . . , 10.0} for the classifier scale w. We employ 250 timesteps to sample from the baseline DDPM... See Table 2 for explicit details. |