Abstract: Aiming at the problem of the need to improve  confidence level in few-shot image classification inference at present, we proposed 
a new model that combined meta-confidence transductive inference, data obfuscation method, and feature-wise linear modulation method. Firstly, by using transductive inference, the model could learn properties of inference data during training process, and achieve targeted learning. Secondly, combining  data obfuscation methods  in the network architecture to enhance the extraction of key features, and  improve the feature discovery ability of the  model.  Finally, feature-wise linear modulation transformation was added to  the transductive inference framework to improve the model’s few-shot query capabilities. The results of experiments conducted on  standard datasets Mini-ImageNet and Tiered-ImageNet show  that the model improves  accuracy by  3.21 and 3.36 percentage points respectively when performing  5-way 1-shot tasks on these two datasets, and by 2.89 and 1.89 percentage points  respectively on 5-way 5-shot tasks. The experimental results validate the effectiveness of the proposed method.

Key words: few-shot learning, transductive inference, data perturbation, feature-wise linear modulation transformation