ConvexVST: A Convex Optimization Approach to Variance-stabilizing Transformation
Authors: Mengfan Wang, Boyu Lyu, Guoqiang Yu
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrated the new approach on bioimaging data and achieved superior performance compared to peer algorithms in terms of not only the variance homoscedasticity but also the impact on subsequent analysis such as denoising. |
| Researcher Affiliation | Academia | 1Bradley Department of Electrical and Computer Engineering, Virginia Tech, VA, USA. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source codes are available at https://github.com/yu-lab-vt/Convex VST. |
| Open Datasets | Yes | In this experiment, we utilized the Fluorescence Microscopy Denoising (FMD) dataset (Zhang et al., 2019b) dedicated to Poissonian-Gaussian denoising to evaluate the methods. |
| Dataset Splits | No | The paper mentions training and test sets but does not explicitly describe a separate validation set or its split. For real data: 'For each subset, we allocated half of the images (25 images of all FOVs) as the training set and half of them as the test set.' |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. It only implies computations were performed. |
| Software Dependencies | No | The paper mentions using 'BM3D (Dabov et al., 2007)' but does not specify version numbers for any software components, libraries, or programming languages used (e.g., Python, PyTorch, etc.). |
| Experiment Setup | No | The paper describes the noise models and dataset usage but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or system-level training settings for their proposed method or the peer methods. |