Residual networks (ResNets) have been utilized for various computer vision and image processing applications. The residual connection improves the training of the network with better gradient flow. A residual block consists of few convolutional layers having trainable parameters, which leads to overfitting. Moreover, the present residual networks are not able to utilize the high and low frequency information suitably, which also challenges the generalization capability of the network. In this paper, a frequency disentangled residual network (FDResNet) is proposed to tackle these issues. Specifically, FDResNet includes separate connections in the residual block for low and high frequency components, respectively. Basically, the proposed model disentangles the low and high frequency components to increase the generalization ability. Moreover, the computation of low and high frequency components using fixed filters further avoids the overfitting. The proposed model is tested on benchmark CIFAR10/100, Caltech and TinyImageNet datasets for image classification. The performance of the proposed model is also tested in image retrieval framework. It is noticed that the proposed model outperforms its counterpart residual model. The effect of kernel size and standard deviation is also evaluated. The impact of the frequency disentangling is also analyzed using saliency map.
翻译:此外,目前遗留的网络无法适当利用高低频信息,这也对网络的普及能力提出了挑战。在本文件中,提议建立一个频率分解的剩余网络(FDREResNet)来解决这些问题。具体地说,FDRESNet包括了低频和高频部分在剩余区块中分别连接,基本上,拟议的模型分离低高频部分,以提高一般化能力。此外,使用固定过滤器计算低高频部分和低高频部分,进一步避免了过度配置。拟议的模型在基准CIFAR10100、Caltech和TinyyImageNet数据集中测试,用于图像分类。提议的模型的性能也在图像检索框架中测试。注意到拟议的模型超越了对应的残余模型。还用地图频率和标准偏差进行分析。还评估了地图频率和标准偏差的影响。