D-PCN: Parallel Convolutional Neural Networks for Image Recognition in Reverse Adversarial Style
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In this paper, a recognition framework named D-PCN using a discriminator is proposed, which can intensify the feature extracting ability of convolutional neural networks. The framework contains two parallel convolutional neural networks, and a discriminator, which is introduced from the Generative Adversarial Nets and can improve the performance of parallel networks. The two nets are devised side by side, and the discriminator takes in the features from parallel networks as input, aiming to guide the two nets to learn features of different details in a reverse adversarial style. After that, the feature maps from two nets get aggregated, then an extra overall classifier is added and will output the final prediction employing the fused features. The training strategy of the D-PCN is also introduced which ensures the utilization of the discriminator. We experiment the D-PCN with several CNN models including NIN, ResNet, ResNeXt and DenseNet using single NVIDIA TITAN Xp, on the two benchmark datasets: CIFAR-100 and downsampled ImageNet-1k, the D-PCN enhances all models on CIFAR-100 and also reinforces the performance of ResNet on downsampled ImageNet-1k explicitly. In particular, it yields state-of-the-art classification performance on CIFAR-100 with compared to relative works.
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