Abstract: Deep learning based on neural network has achieved excellent results in a large number of fields,but it is difficult to deal with similar or untrained tasks,and it is difficult to learn and adapt to new tasks.Moreover,it requires a high scale of trai-ning samples,resulting in its poor generalization and expansion.Meta learning is a new learning framework,which aims to solve the problem that traditional learning methods can’t solve fast learning and adapt to new tasks.Aiming at the meta learning problem of image classification,a novel fiber bundle meta learning algorithm based on Bayesian theory is proposed.Firstly,the convolution neural network is used to extract the image information supporting the dataset,and the image representation is obtained.Then the manifold structure of data features and the fiber bundle of data features are constructed.The input query set selects the manifold section of the current new task to obtain the fiber suitable for the new task,so as to get the correct label of the image.Experimental results show that the model based on the proposed algorithm (FBBML) achieves the best accuracy performance compared with the standard four-layer convolutional neural network model on the common data set (mini-ImageNet).At the same time,the fiber bundle theory is introduced into meta learning,which makes the algorithm more interpretable.

Key words: Bayesian, Classification, Convolution neural network, Fiber bundle, Manifold, Meta learning