Mixup is a learning principle that trains a neural network on convex combinations of pairs of examples and their labels. Despite of its good performance, there is an inherent inconsistency between training and testing in mixup, which makes theoretical understanding difficult and hurts the performance in some cases. In this work, we propose λ-mixup to alleviate this inconsistency. Specifically, λ-mixup reformulates the model to take the interpolation coefficient (𝜆) as input as well, so that a class of models indexed by 𝜆 is learned and we can select one specific coefficient or multiple coefficients for ensembles depending on the testing distribution. We theoretically demonstrate that, with enough data and model capacity, λ-mixup can recover the original conditional distribution. Moreover, we conduct image classification tasks on multiple datasets, including CIFAR-10, CIFAR-100 and Tiny-Imagenet, showing that comparing with mixup, λ-mixup exhibits better generalization, calibration and robustness to adversarial attacks and out-of-distribution transformations.
Partial fingerprint identification technology which is mainly used in device with small sensor area like cellphone, U disk and computer, has taken more attention in recent years with its unique advantages. However, owing to the lack of sufficient minutiae points, the conventional method do not perform well in the above situation. We propose a new fingerprint matching technique which utilizes ridges as features to deal with partial fingerprint images and combines the modified generalized Hough transform and scoring strategy based on machine learning. The algorithm can effectively meet the real-time and space-saving requirements of the resource constrained devices. Experiments on in-house database indicate that the proposed algorithm have an excellent performance.
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