Few-Shot Learning Based on Information Geometry

Few-shot classification, as the name suggests, is a very challenging task where only a few training examples are available for each category. Extensive research has been done to solve the problem of the high cost of labeling a large number of samples. A recent research strategy is to find similarity measures between query sets and support sets for judgment. Recently, information geometry techniques have been applied in deep learning research, which explores the relationships and properties between probability distributions by treating them as geometric objects and using geometric tools. Based on information geometry, this paper characterizes differences in the geometric properties of images as differences in probability distributions. Assume that the data satisfies a multivariate Gaussian distribution and that different images are considered as different points on the manifold according to the parameters that characterize the probability distribution of the images. We measure the similarity between different images by the geodesic and validate three methods for estimating the geodesic on a neural network model with 64 neurons at four levels and improve the model performance by Firth bias. The GM, GMF, KM, and KMF methods are validated in the resnet-12 and resnet-18 frameworks using a meta-learning approach on the miniImageNet and CUB datasets, respectively. The experiments show that the models trained by the above four algorithms have good performance and have advantages over the classical algorithms.