Convolutional neural networks (CNNs) are widely used to tackling complex tasks, which are prone to overfitting if the datasets are noisy. Therefore, we propose folding fan cropping and splicing (FFCS) regularization strategy to enhance representation abilities of CNNs. In particular, we propose two different methods considering the effect of different segmentation numbers on classification results. One is the random folding fan method, and the other is the fixed folding fan method. Experimental results showed that FFCS reduced the classification errors both with the value of 0.88% on CIFAR-10 dataset and 1.86% on ImageNet dataset. Moreover, FFCS consistently outperformed Mixup and Random Erasing approaches on classification tasks. Therefore, FFCS effectively prevents overfitting and reduces the impact of background noises on classification tasks.
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