  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.
