<jupyter_start><jupyter_text>Reproducing the results Visualizing attention mapsFirst we reproduce the attention maps for the baseline FactorVAE and the best performing AD-FactorVAE using $\lambda=40$ and the first convolutional layer. Press any key to close the image. (Using the --help flag you can find more options)<jupyter_code>!python visualizer.py --name FactorVAE --target_layer 0 --cuda --sample_count 10
!python visualizer.py --name AD-FactorVAE --target_layer 0 --cuda --sample_count 10<jupyter_output>loaded checkpoint 'checkpoints/AD-FactorVAE'<jupyter_text>Plotting the resultsNext we plot the results for all the models used, the last one is the one used in the paper. (Using the --help flag you can find more options)<jupyter_code>%run plotter.py --names 'gamma40 lambda1_gamma40 lambda20_gamma40 lambda40_gamma40 conv3_lambda1_gamma40' --all_plots
cd FactorVAE
ls<jupyter_output>[0m[01;34mcheckpoints[0m/        environment.yml  model.py      README.md      solver.py
[01;34mdata[0m/               gradcam.py       ops.py        [01;34mresults[0m/       utils.py
dataset.py          LICENSE          plotter.py    results.ipynb  visualizer.py
disentanglement.py  main.py          [01;34m__pycache__[0m/  [01;34mscripts[0m/
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