  We present PAODING, a toolkit to debloat pretrained neural network models
through the lens of data-free pruning. To preserve the model fidelity, PAODING
adopts an iterative process, which dynamically measures the effect of deleting
a neuron to identify candidates that have the least impact to the output layer.
Our evaluation shows that PAODING can significantly reduce the model size,
generalize on different datasets and models, and meanwhile preserve the model
fidelity in terms of test accuracy and adversarial robustness. PAODING is
publicly available on PyPI via https://pypi.org/project/paoding-dl.
