论文标题
通过学习恢复原型来进行单拍图像分类
One-Shot Image Classification by Learning to Restore Prototypes
论文作者
论文摘要
一击图像分类旨在在数据集上训练图像分类器，每个类别仅一个图像。对于现代深层神经网络而言，这是一个挑战，通常需要每班需要数百或数千张图像。在本文中，我们通过比较特征空间中的测试图像与每个类别的中心之间的距离，对此问题采用了公制学习，该问题已应用于少量和许多图像分类。但是，对于一次性学习，现有的度量学习方法的性能会差，因为单个训练图像可能无法代表该类。例如，如果图像远离特征空间中的班级中心，则基于度量学习的算法不太可能对测试图像做出正确的预测，因为该嘈杂的图像会移动决策边界。为了解决这个问题，我们提出了一个简单而有效的回归模型，该模型由RestorEnet表示，该模型在图像功能上学习了类不可知的转换，以将图像移至特征空间中的类中心。实验表明，RestoreNet在广泛的数据集上的最先进方法获得了卓越的性能。此外，Restorenet可以轻松地与其他方法结合起来，以实现进一步的改进。
One-shot image classification aims to train image classifiers over the dataset with only one image per category. It is challenging for modern deep neural networks that typically require hundreds or thousands of images per class. In this paper, we adopt metric learning for this problem, which has been applied for few- and many-shot image classification by comparing the distance between the test image and the center of each class in the feature space. However, for one-shot learning, the existing metric learning approaches would suffer poor performance because the single training image may not be representative of the class. For example, if the image is far away from the class center in the feature space, the metric-learning based algorithms are unlikely to make correct predictions for the test images because the decision boundary is shifted by this noisy image. To address this issue, we propose a simple yet effective regression model, denoted by RestoreNet, which learns a class agnostic transformation on the image feature to move the image closer to the class center in the feature space. Experiments demonstrate that RestoreNet obtains superior performance over the state-of-the-art methods on a broad range of datasets. Moreover, RestoreNet can be easily combined with other methods to achieve further improvement.
