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VAIM

This is the implementation of Variational Autoencoder Inverse Mapper: An End-to-End Deep Learning Framework for Inverse Problems (VAIM). (https://ieeexplore.ieee.org/document/9534012)

Requirements

The code is written in Python=3.6, with the following libraries:

  • tensorflow==1.11.0
  • keras==2.1.2

Getting started

  • Install the python libraries. (See Requirements).
  • Download the code from GitHub:
git clone https://github.com/alanaziyasir/VAIM
cd VAIM
  • Run the python script:
python3 train.py
  • By default the script will run the first toy example which is f(x) = x2.
  • To run another example, adjust self.example variable in line 12 in VAIM.py.
  • To see the jupyter notebbok demo go to VAIM_demo.ipynb.

Results:

  • The script will create a directory saved_model/ and save the the weights with the lowest validation error
  • It will also plot the latent and the results

Example plots after 5k epochs:

f(x) = x2 f(x) = sin(x)
latent of x2 latent of sin(x)