Training Unbiased Diffusion Models From Biased Dataset
Authors: Yeongmin Kim, Byeonghu Na, Minsang Park, JoonHo Jang, Dongjun Kim, Wanmo Kang, Il-chul Moon
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental evidence supports the usefulness of the proposed method, which outperforms baselines including time-independent importance reweighting on CIFAR-10, CIFAR-100, FFHQ, and Celeb A with various bias settings. |
| Researcher Affiliation | Collaboration | Yeongmin Kim1 , Byeonghu Na1, Minsang Park1, Joon Ho Jang1, Dongjun Kim1, Wanmo Kang1, Il-Chul Moon1,2 (...) 1KAIST, 2Summary.AI |
| Pseudocode | Yes | Algorithm 1: Discriminator Training algorithm (...) Algorithm 2: Score Training algorithm with TIW-DSM |
| Open Source Code | Yes | Our code is available at https://github.com/alsdudrla10/TIW-DSM. |
| Open Datasets | Yes | We consider CIFAR-10, CIFAR-100, FFHQ, and Celeb A datasets, which are commonly used for generative learning. |
| Dataset Splits | No | The paper does not explicitly provide the specific percentages or counts for training/validation/test dataset splits from the observed dataset (Dbias) to reproduce the experiment's data partitioning. |
| Hardware Specification | Yes | Table 7 shows the computational costs measured using RTX 4090 4 cores in the CIFAR-10 experiments. |
| Software Dependencies | No | The paper mentions using PyTorch and following procedures from EDM, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Table 6: Training and sampling configurations. |