The significant success of Deep Neural Networks (DNNs) relies on the availability of annotated large-scale datasets. However, it is time-consuming and expensive to obtain the available annotated datasets of huge size, which hinders the development of DNNs. In this paper, a novel two-stage framework is proposed for learning with noisy labels, called Two-Stage Sample selection and Semi-supervised learning Network (TSS-Net). It combines sample selection with semi-supervised learning. The first stage divides the noisy samples from the clean samples using cyclic training. The second stage uses the noisy samples as unlabeled data and the clean samples as labelled data for semi-supervised learning. Unlike previous approaches, TSS-Net does not require specifically designed robust loss functions and complex networks. It achieves decoupling of the two stages, which means that each stage can be replaced with a superior method to achieve better results, and this improves the inclusiveness of the network. Our experiments are conducted on several benchmark datasets in different settings. The experimental results demonstrate that TSS-Net outperforms many state-of-the-art methods.
With the continuous development of multimedia technology and shooting hardware, more and more high-quality images appear in life and production. At present, there are many image encryption methods to ensure image security, but most of them cannot meet the real-time requirements of large-size image encryption, which causes great obstacles to the application and popularization of image encryption. In this paper, an efficient image encryption method based on variable row-columns scrambling and dynamic threshold selective block diffusion is designed. The pixels are operated in batches row by column and block, and the length of chaotic sequence required is reduced without reducing the security, thus reducing the time consuming of the encryption system. In addition, the modified five-point sampling is adopted to select chaotic sequence, which improves the utilization rate of chaotic sequence and further improves the encryption efficiency. Experimental results show that the proposed method has a higher encryption efficiency than the existing methods with similar security, and has strong practicability.
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