Abstract：In scene classification based on convolutional neural network, over-fitting is caused due to the large number of network training and poor convergence performance with the small training dataset. To eliminate the negative effect, an algorithm for scene classification with adaptive learning rate and sample training mode is proposed. The network learning rate is adaptively adjusted on the framework of convolutional neural network according to the variation of the error function in the network training. When the error function changes slightly, the learning rate of the batch is unchanged. When the error function changes more remarkably, the variation of the learning rate is inversely proportional to the variation of the error function. Meanwhile, according to the network output, the sample training mode is switched, and the inaccurately recognized images are emphatically trained. The experimental results on Scene-15 and Cifar-10 scene datasets show that the proposed method improves the convergence of neural networks and effectively improves the classification accuracy, especially the classification accuracy of complex scenes such as indoor scenes.
储珺, 苏亚伟, 王璐. 自适应调节学习率和样本训练方式的场景分类[J]. 模式识别与人工智能, 2018, 31(7): 625-633.
CHU Jun, SU Yawei, WANG Lu. Scene Classification with Adaptive Learning Rate and Sample Training Mode. , 2018, 31(7): 625-633.
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