  The past few years have witnessed the fast development of different
regularization methods for deep learning models such as fully-connected deep
neural networks (DNNs) and Convolutional Neural Networks (CNNs). Most of
previous methods mainly consider to drop features from input data and hidden
layers, such as Dropout, Cutout and DropBlocks. DropConnect select to drop
connections between fully-connected layers. By randomly discard some features
or connections, the above mentioned methods control the overfitting problem and
improve the performance of neural networks. In this paper, we proposed two
novel regularization methods, namely DropFilter and DropFilter-PLUS, for the
learning of CNNs. Different from the previous methods, DropFilter and
DropFilter-PLUS selects to modify the convolution filters. For DropFilter-PLUS,
we find a suitable way to accelerate the learning process based on theoretical
analysis. Experimental results on MNIST show that using DropFilter and
DropFilter-PLUS may improve performance on image classification tasks.
