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..
Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation
Authors: Yaoru Luo, Guole Liu, Yuanhao Guo, Ge Yang1908-1916
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To quantitatively analyze the segmentation performance of DNNs trained by these labels, we experiment on two representative segmentation models, U-Net (Ronneberger, Fischer, and Brox 2015) and Deep Labv3+ (Chen et al. 2018), with the same loss function (binary cross-entropy loss) and optimizer (stochastic gradient descent, SGD). |
| Researcher Affiliation | Academia | 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2School of Artificial Intelligence, University of Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1: Unsupervised Iteration Strategy |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the methodology described is publicly available. |
| Open Datasets | Yes | For binary-class segmentation, we select fluorescence microscopy images of ER, MITO datasets (Luo, Guo, and Yang 2020) and the NUC dataset (Caicedo et al. 2019). For multi-class segmentation, we select natural images of Cityscapes dataset (Cordts et al. 2016). |
| Dataset Splits | No | The paper mentions 'testing dice scores during training' but does not explicitly provide specific training/validation/test dataset splits (e.g., percentages, counts, or detailed methodology) in the main text. It refers to Appendix C for 'Detailed information on the datasets and experimental configurations', but this information is not directly in the main body. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of U-Net and Deep Labv3+ models, binary cross-entropy loss, and stochastic gradient descent (SGD) optimizer, but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | No | The paper mentions using U-Net and Deep Labv3+ models, binary cross-entropy loss, and SGD optimizer. It states that 'Detailed information on the datasets and experimental configurations are provided in Appendix C.', implying hyperparameters are not detailed in the main text. |