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..
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 | Venue PDF | 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. |