Spike buffer: improve deep network performance by offset mechanism

Abstract

For a well-designed neural network model, it is difficult to further improve its performance. This study proposes an offset mechanism called spike buffer, which can effectively improve the performance of the designed convolutional neural networks. The spike buffer introduces an offset buffer-bit and a gradient spike function in the convolution channels to enhance the expression of effective features and suppresses the extraction of invalid features. Without significantly increasing the computational complexity of deep convolution neural networks, it can improve the feature selection performance of convolution neural networks and enhance the ability of non-linear mapping, and can be easily embedded into various convolution neural networks. Experiments show that the performance of convolutional neural networks with integrated spike buffer can be effectively improved.

Keywords

• computational complexity
• convolutional neural nets
• feature selection
• feature extraction
• feature selection performance
• integrated spike buffer
• deep network performance
• offset mechanism
• offset buffer-bit
• gradient spike function
• convolution channels
• deep convolution neural networks
• invalid feature extraction
• computational complexity
• nonlinear mapping