In this paper, we propose a simple but very effective approach in the presence of noisy labels. The memorization effects of Deep Neural Networks (DNNs) manifests that they first memorize training data with clean labels and memorize data with noisy labels gradually. Based on this phenomenon, we build Class Prediction Distributions(CPD) for each sample in the initial stage of network training. On the basis of CPD, we use our clean data selection strategy to divide training data into confidently clean data and noisy data. In this selection strategy, we rank the maximum value of CPD. Top-ranked samples are more likely to be clean samples. Finally, noisy labels classification is successfully achieved by using semi-supervised learning. Experiments on benchmark datasets including MNIST, Cifar-10, Cifar-100 and Clothing1M demonstrate that our approach can achieve a competitive performance.
In infrared small target detection tasks, targets usually occupy very few pixels and present as local bright spots, lacking prior knowledge such as shape and speed. In response to the above problems, a temporal low-rank and sparse decomposition and spatio-temporal continuity detection algorithm, names as TLRSD-STC, is proposed to detect small targets and eliminate false alarm targets. The proposed algorithm firstly expands the sequence images in time domain. The preliminary separation of small targets and background is achieved through low-rank and sparse decomposition, and target prediction maps can be obtained. Subsequently, targets and noise are further separated by an improved pipeline filter to obtain the final detection image. The proposed algorithm is validated on three sequence images containing complex scenes. Experimental results demonstrate that the algorithm has a higher detection rate and lower false alarm rate than other algorithms in complex scenes.
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