Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Experts
Authors: Zhitong Gao, Yucong Chen, Chuyu Zhang, Xuming He
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our method on the LIDC-IDRI dataset and a modified multimodal Cityscapes dataset. Results demonstrate that our method achieves the state-of-the-art or competitive performance on all metrics. 1 |
| Researcher Affiliation | Academia | 1Shanghai Tech University, Shanghai, China 2Lingang Laboratory, Shanghai, China 3Shanghai Engineering Research Cente of Intelligent Vision and Imaging, Shanghai, China {gaozht,chenyc,zhangchy2,hexm}@shanghaitech.edu.cn |
| Pseudocode | Yes | The overall training procedure is shown in algorithm 1. We use the weighted version of our nonparametric representation during training, as displayed in lines 3 9. [...] Algorithm 1 Training Procedure |
| Open Source Code | Yes | The complete source code and trained models are publicly released at https://github.com/ gaozhitong/Mo SE-AUSeg. |
| Open Datasets | Yes | We validate our method on the LIDC-IDRI dataset (Armato III et al., 2011) and a modified multimodal Cityscapes dataset (Cordts et al., 2016; Kohl et al., 2018). |
| Dataset Splits | Yes | For fair comparison, we use a preprocessed 2D dataset provided by Kohl et al. (2018) with 15096 slices each cropped to 128 128 patches and adopt the 60-20-20 dataset split manner same as Baumgartner et al. (2019); Monteiro et al. (2020). |
| Hardware Specification | Yes | We use two NVIDIA TITAN RTX GPUs on the LIDC dataset and four NVIDIA TITAN RTX GPUs on the Cityscapes dataset. |
| Software Dependencies | No | The paper mentions 'PyTorch implementation' and 'Adam optimizer' but does not specify version numbers for PyTorch or any other software libraries, compilers, or operating systems used. |
| Experiment Setup | Yes | On the LIDC dataset, we use K = 4 experts each with S = 4 samples. In the loss function, we use the Io U (Milletari et al., 2016) as the pair-wise cost function and set the hyperparameters γ0 = 1/2, β = 1 for full-annotation case and β = 10 for one-annotation case. On the Cityscapes dataset, we use a slightly larger number K = 35 of experts each with S = 2 samples. We use the CE as the pair-wise cost function and set the hyperparameters γ0 = 1/32, β = 1 for the loss. To stabilize the training, we adopt a gradient-smoothing trick in some cases and refer the reader to Appendix A.1 for details. |