Divergence Regulated Encoder Network:

Divergence Regulated Encoder Network For Joint Dimensionality Reduction And Classification

Joshua Peeples, Sarah Walker, Connor McCurley, Alina Zare, James Keller and Weihuang Xu

Note: If this code is used, cite it: Joshua Peeples, Sarah Walker, Connor McCurley, Alina Zare, James Keller, & Weihuang Xu. (2020, December 30). GatorSense/DREN: Initial Release (Version v1.0). Zenodo. https://doi.org/10.5281/zenodo.4404604

[IEEE GRSL]

[arXiv]

[BibTeX]

In this repository, we provide the paper and code for Divergence Regulated Encoder Network (DREN) models from "Divergence Regulated Encoder Network For Joint Dimensionality Reduction And Classification"

Installation Prerequisites

This code uses python, pytorch, and barbar. Please use [Pytorch's website] to download necessary packages. Barbar is used to show the progress of model. Please follow the instructions [here] to download the module.

Demo

Run demo.py in Python IDE (e.g., Spyder) or command line. To evaluate performance, run View_Results.py (if results are saved out).

Main Functions

The Divergence Regulated Encoder Network (DREN) runs using the following functions.

1. Intialize model

model, input_size = intialize_model(**Parameters)

2. Prepare dataset(s) for model

dataloaders_dict = Prepare_Dataloaders(**Parameters)

3. Train model

train_dict = train_model(**Parameters)

4. Test model

test_dict = test_model(**Parameters)

Parameters

The parameters can be set in the following script:

Demo_Parameters.py

Inventory

https://github.com/GatorSense/DREN

└── root dir
    ├── demo.py   //Run this. Main demo file.
    ├── Demo_Parameters.py // Parameters file for demo.
    ├── Capture_Metrics.py // Save validation and test performance in spreadsheet.
    ├── Convergence_Analysis.py // Analyze convergence of models for each dataset.
    ├── Prepare_Data.py  // Load data for demo file.
    ├── Texture_Information.py // Class names and directories for datasets.
    ├── View_Results.py // Run this after demo to view saved results.
    ├── knn_experiment.py // Trains and tests a KNN with the embeddings produced by the model and embeddings produced through t-SNE
    ├── Out_of_Sample.py // Produces an embedding with out of sample points by learning the manifold of the original embedding
    ├── papers  // Related publications.
    │   ├── readme.md //Information about paper
    └── Utils  //utility functions
        ├── Compute_FDR.py  // Compute Fisher Discriminant Ratio for features.
        ├── Confusion_mats.py  // Generate confusion matrices.
        ├── Embedding_Model.py  // Generates model with an encoder following the final layer 
        ├── Generate_Embedding_Vid.py  // Generates a video showing how the embedding of the model changes with each epoch
        ├── Generate_Histogram_Vid.py  // Generates a video showing how the histogram layer varies with each epoch
        ├── Generate_Learning_Curves.py  // Generates the learning curves for the model
        ├── Generate_TSNE_visual.py  // Generate TSNE visualization for features.
        ├── Histogram_Model.py  // Generate HistRes_B models.
        ├── Network_functions.py  // Contains functions to initialize, train, and test model. 
        ├── Plot_Accuracy.py // Plots the average and std of metrics for each model
        ├── Plot_Decision_Boundary.py // Plots the decision boundary found by the model.    
        ├── RBFHistogramPooling.py  // Create histogram layer. 
        ├── Save_Results.py  // Save results from demo script.
        ├── TSNE_Loss.py  // Includes functions to compute the embedding loss found by t-SNE methods
     

License

This source code is licensed under the license found in the LICENSE file in the root directory of this source tree.

This product is Copyright (c) 2022 J. Peeples, S. Walker, C. McCurley, A. Zare, J. Keller, & W. Xu. All rights reserved.

Citing Divergence Regulated Encoder Network (DREN)

If you use the Divergence Regulated Encoder Network (DREN) code, please cite the following reference using the following entry.

Plain Text:

J. Peeples, S. Walker, C. Mccurley, A. Zare, J. Keller and W. Xu, "Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 3511305, doi: 10.1109/LGRS.2022.3156532.

BibTex:

@ARTICLE{peeples2022divergence,
  author={Peeples, Joshua and Walker, Sarah and Mccurley, Connor and Zare, Alina and Keller, James and Xu, Weihuang},
  journal={IEEE Geoscience and Remote Sensing Letters}, 
  title={Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification}, 
  year={2022},
  volume={19},
  number={},
  pages={1-5},
  doi={10.1109/LGRS.2022.3156532}
  }