少量学习的免费午餐：分布校准

从有限数量的样本中学习具有挑战性，因为基于仅几个训练示例形成的偏差分布，学习的模型很容易变得过拟合。在本文中，我们通过传递带有足够示例的统计信息来校准这些样本数量较少的类别的分布，然后可以从校准后的分布中抽取足够数量的样本以扩展输入到分类器的数量。..

Free Lunch for Few-shot Learning: Distribution Calibration

Learning from a limited number of samples is challenging since the learned model can easily become overfitted based on the biased distribution formed by only a few training examples. In this paper, we calibrate the distribution of these few-sample classes by transferring statistics from the classes with sufficient examples, then an adequate number of examples can be sampled from the calibrated distribution to expand the inputs to the classifier.We assume every dimension in the feature representation follows a Gaussian distribution so that the mean and the variance of the distribution can borrow from that of similar classes whose statistics are better estimated with an adequate number of samples. Our method can be built on top of off-the-shelf pretrained feature extractors and classification models without extra parameters. We show that a simple logistic regression classifier trained using the features sampled from our calibrated distribution can outperform the state-of-the-art accuracy on two datasets (~5% improvement on miniImageNet compared to the next best). The visualization of these generated features demonstrates that our calibrated distribution is an accurate estimation.