Binary Stochastic Filtering (BSF), the algorithm for feature selection and neuron pruning is proposed in this work. Filtering layer stochastically passes or filters out features based on individual weights, which are tuned during neural network training process. By placing BSF after the neural network input, the filtering of input features is performed, i.e. feature selection. More then 5-fold dimensionality decrease was achieved in the experiments. Placing BSF layer in between hidden layers allows filtering of neuron outputs and could be used for neuron pruning. Up to 34-fold decrease in the number of weights in the network was reached, which corresponds to the significant increase of performance, that is especially important for mobile and embedded applications.
翻译:在这项工作中提议了地物选择和神经元剪切算法(BSF),即地物选择和神经元剪切的算法。过滤层透析或根据神经网络培训过程中的单个重量过滤功能。通过在神经网络输入后放入BSF,对输入特性进行过滤,即特征选择。在实验中实现了5倍以上的尺寸降低。将隐藏层之间的BSF层用于过滤神经元输出,并可用于神经剪切处理。网络重量减少34倍以上,这与性能的显著提高相对应,对于移动和嵌入应用尤其重要。