Automatic metrics learning with low‐noise embedding for zero‐shot learning.

This Letter presents a new improved zero‐shot learning (ZSL) method based on meta‐learning that automatically adjusts metrics by multi‐layer perceptrons instead of using traditional Euclidean distance, cosine distance and other fixed metrics. In this way, the authors directly train the visual and semantic joint information of the images without adding additional auxiliary, which simply and effectively avoids the error derivation of unseen classes due to improper fixed metrics. Smooth one‐hot encoding rule is used to make the calculation of loss more reasonable. They also discuss the embedding methods to find a suitable low‐noise embedding space and analyse the model performance under the conventional setting and popular generalised ZSL setting. Experimental results show that the proposed automatic metrics method outperforms existing state‐of‐the‐art approaches in some parts of AwA2 and CUB datasets. [ABSTRACT FROM AUTHOR]