Uncertainty-based Network for Few-shot Image Classification
This work addresses few-shot learning for image classification, but it is incremental as it builds on existing transductive inference techniques.
The paper tackles the problem of few-shot image classification by proposing an uncertainty-based network that models classification uncertainty using mutual information, achieving comparable accuracy to state-of-the-art methods on four benchmarks.
The transductive inference is an effective technique in the few-shot learning task, where query sets update prototypes to improve themselves. However, these methods optimize the model by considering only the classification scores of the query instances as confidence while ignoring the uncertainty of these classification scores. In this paper, we propose a novel method called Uncertainty-Based Network, which models the uncertainty of classification results with the help of mutual information. Specifically, we first data augment and classify the query instance and calculate the mutual information of these classification scores. Then, mutual information is used as uncertainty to assign weights to classification scores, and the iterative update strategy based on classification scores and uncertainties assigns the optimal weights to query instances in prototype optimization. Extensive results on four benchmarks show that Uncertainty-Based Network achieves comparable performance in classification accuracy compared to state-of-the-art method.