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..
Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels
Authors: Jian Chen, Ruiyi Zhang, Tong Yu, Rohan Sharma, Zhiqiang Xu, Tong Sun, Changyou Chen
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted for evaluation. Our model achieves new state-of-the-art (SOTA) results on all standard real-world benchmark datasets. |
| Researcher Affiliation | Collaboration | Jian Chen1 Ruiyi Zhang2 Tong Yu2 Rohan Sharma1 Zhiqiang Xu3 Tong Sun2 Changyou Chen1 1University at Buffalo 2Adobe Research 3MBZUAI |
| Pseudocode | Yes | Algorithm 1 Training Input: training set {X, Y}, image encoder fp, fq. |
| Open Source Code | Yes | Code is available at https://github.com/puar-playground/LRA-diffusion |
| Open Datasets | Yes | We conduct simulation experiments on the CIFAR-10 and CIFAR-100 datasets [51] to evaluate our method s performance under various noise types. |
| Dataset Splits | Yes | The dataset includes a clean training set, validation set, and test set with manually refined labels, consisting of approximately 47.6k, 14.3k, and 10k pictures, respectively. |
| Hardware Specification | Yes | All experiments were done on four NVIDIA Titan V GPUs. |
| Software Dependencies | No | No specific software versions (e.g., library names with version numbers) are mentioned in the paper. |
| Experiment Setup | Yes | We train LRA-diffusion models for 200 epochs with Adam optimizer. The batch size is 256. We used a learning rate schedule that included a warmup phase followed by a half-cycle cosine decay. The initial learning rate is set to 0.001. |