Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Paoding: Supervised Robustness-preserving Data-free Neural Network
  Pruning   When deploying pre-trained neural network models in real-world applications,
model consumers often encounter resource-constraint platforms such as mobile
and smart devices. They typically use the pruning technique to reduce the size
and complexity of the model, generating a lighter one with less resource
consumption. Nonetheless, most existing pruning methods are proposed with a
premise that the model after being pruned has a chance to be fine-tuned or even
retrained based on the original training data. This may be unrealistic in
practice, as the data controllers are often reluctant to provide their model
consumers with the original data. In this work, we study the neural network
pruning in the \emph{data-free} context, aiming to yield lightweight models
that are not only accurate in prediction but also robust against undesired
inputs in open-world deployments. Considering the absence of the fine-tuning
and retraining that can fix the mis-pruned units, we replace the traditional
aggressive one-shot strategy with a conservative one that treats the pruning as
a progressive process. We propose a pruning method based on stochastic
optimization that uses robustness-related metrics to guide the pruning process.
Our method is implemented as a Python package named \textsc{Paoding} and
evaluated with a series of experiments on diverse neural network models. The
experimental results show that it significantly outperforms existing one-shot
data-free pruning approaches in terms of robustness preservation and accuracy.

15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an