Separable Convolutional Eigen-Filters (SCEF): Building Efficient CNNs Using Redundancy Analysis
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The high model complexity of deep learning algorithms enables remarkable learning capacity in many application domains. However, a large number of trainable parameters comes with a high cost. For example, during both the training and inference phases, the numerous trainable parameters consume a large amount of resources, such as CPU/GPU cores, memory and electric power. In addition, from a theoretical statistical learning perspective, the high complexity of the network can result in a high variance in its generalization performance. One way to reduce the complexity of a network without sacrificing its accuracy is to define and identify redundancies in order to remove them. In this work, we propose a method to observe and analyze redundancies in the weights of a 2D convolutional (Conv2D) network. Based on the proposed analysis, we construct a new layer called Separable Convolutional Eigen-Filters (SCEF) as an alternative parameterization to Conv2D layers. A SCEF layer can be easily implemented using depthwise separable convolution, which are known to be computationally effective. To verify our hypothesis, experiments are conducted on the CIFAR-10 and ImageNet datasets by replacing the Conv2D layers with SCEF and the results have shown an increased accuracy using about 2/3 of the original parameters and reduce the number of FLOPs to 2/3 of the original net. Implementation-wise, our method is highly modular, easy to use, fast to process and does not require any additional dependencies.
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