Embedded adaptive cross-modulation neural network for few-shot learning

Although deep neural networks have made great success in several scenarios of machine learning, they face persistent challenges in small training datasets learning scenarios. Few-shot learning aims to learn from a few labeled examples. However, the limited training samples and weakly distinguishable embedding vectors in a metric space often lead to unsatisfactory test results and directly calculating the distance between tensors can cause ambiguity. This paper proposes an embedded adaptive cross-modulation (EACM) method for few-shot learning which combines the information between support and query examples. Specifically, the inter-class categorizability between the support set prototype representations is enhanced by the adaptive cosine metric module to improve the accuracy of the few-shot recognition result. The learning is performed by using the cross-modulation module at many levels of abstraction layers along the prediction pipeline. The support set and query set feature cross-enhance, which improves the generalization ability and robustness of image recognition. Afterward, we further combine above two methods by a weight balance scalar to determine the task-related metric space and construct a joint loss function. Theoretical analysis demonstrates the generalization ability of EACM. We conduct comprehensive experiments on mini-ImageNet and CUB datasets. Experimental results show that our approach is the state-of-the-art approach by significant margins.