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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.
The aim of few-shot learning (FSL) is to learn how to recognize image categories from a small number of training examples. A central challenge is that the available training examples are normally insufficient to determine which visual features are mo
Few-shot learning aims to recognize new categories using very few labeled samples. Although few-shot learning has witnessed promising development in recent years, most existing methods adopt an average operation to calculate prototypes, thus limited
It is well known that humans can learn and recognize objects effectively from several limited image samples. However, learning from just a few images is still a tremendous challenge for existing main-stream deep neural networks. Inspired by analogica
Few-shot image classification is a challenging problem which aims to achieve the human level of recognition based only on a small number of images. Deep learning algorithms such as meta-learning, transfer learning, and metric learning have been emplo
In this paper, we propose a subspace representation learning (SRL) framework to tackle few-shot image classification tasks. It exploits a subspace in local CNN feature space to represent an image, and measures the similarity between two images accord