reproducibilityindex.ai

Label-Noise Robust Diffusion Models

Authors: Byeonghu Na, Yeongmin Kim, HeeSun Bae, Jung Hyun Lee, Se Jung Kwon, Wanmo Kang, Il-chul Moon

Reproducibility Variable	Result	LLM Response
Research Type	Experimental	4 EXPERIMENTS
Researcher Affiliation	Collaboration	Byeonghu Na1, Yeongmin Kim1, Hee Sun Bae1, Jung Hyun Lee2, Se Jung Kwon2, Wanmo Kang1 & Il-Chul Moon1,3 1KAIST, 2NAVER Cloud, 3summary.ai
Pseudocode	Yes	Algorithm 1: Training algorithm with TDSM
Open Source Code	Yes	Our code is available at: https://github.com/byeonghu-na/tdsm.
Open Datasets	Yes	We evaluate our method on three benchmark datasets commonly used for both image generation and label noise learning: MNIST (Le Cun et al., 2010), CIFAR-10, and CIFAR-100 (Krizhevsky, 2009).
Dataset Splits	No	The paper mentions 'training dataset' and 'test dataset' but does not explicitly provide percentages or absolute sample counts for training, validation, and test splits within the main text.
Hardware Specification	Yes	We utilized 8 NVIDIA Tesla P40 GPUs and employed CUDA 11.4 and Py Torch 1.12 versions in our experiments.
Software Dependencies	Yes	We utilized 8 NVIDIA Tesla P40 GPUs and employed CUDA 11.4 and Py Torch 1.12 versions in our experiments.
Experiment Setup	Yes	The score network was trained with a batch size of 512, and the training iterations were set to 400,000 for MNIST and CIFAR-10, 200,000 for CIFAR-100. For Clothing-1M, the score network is trained with a batch size 256 for 200,000 training iterations.