Abstract
The convolution neural networks (CNNs) can extract the rich feature of the image. It was widely used in the field of computer vision (CV) and made great breakthroughs. However, most of the existing CNNs models only utilize the features out put by last layer, the representation of features is not comprehensive enough. In this paper, we propose a multilevel features fusion method, in order to make full use of the intermediate layer features. This method can strengthen feature propagation and improve the accuracy of downstream tasks. We evaluate our method through experiments on two image classification benchmark tasks: CIFAR-10 and CIFAR-100. The experimental results show that our method is able to significantly improve the accuracy of VGG-like model. The improved model is better than most existing models.
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Zhuo, YF., Wang, YL. (2018). Multilevel Features Fusion in Deep Convolutional Neural Networks. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11068. Springer, Cham. https://doi.org/10.1007/978-3-030-00021-9_53
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