  Non-linear activation functions, e.g., Sigmoid, ReLU, and Tanh, have achieved
great success in neural networks (NNs). Due to the complex non-linear
characteristic of samples, the objective of those activation functions is to
project samples from their original feature space to a linear separable feature
space. This phenomenon ignites our interest in exploring whether all features
need to be transformed by all non-linear functions in current typical NNs,
i.e., whether there exists a part of features arriving at the linear separable
feature space in the intermediate layers, that does not require further
non-linear variation but an affine transformation instead. To validate the
above hypothesis, we explore the problem of linear feature disentanglement for
neural networks in this paper. Specifically, we devise a learnable mask module
to distinguish between linear and non-linear features. Through our designed
experiments we found that some features reach the linearly separable space
earlier than the others and can be detached partly from the NNs. The explored
method also provides a readily feasible pruning strategy which barely affects
the performance of the original model. We conduct our experiments on four
datasets and present promising results.

Machine Learning;Artificial Intelligence