To address this challenge, the team proposed an innovative framework called Decoupled Feature Learning (DFL). DFL applied causal inference techniques to mitigate training data bias by constructing diverse training domains and employed the Center Triplet Loss to enhance the model's ability to capture core pest features across different domains.
Researchers tested the new method, DFL, on three different datasets: the Li dataset, Dong's Few-Shot Pest Dataset (DFSPD), and the large-scale IP102 dataset.
These datasets were collections of images used to train and evaluate the accuracy of pest recognition models. Results showed that DFL significantly improved performance, achieving high recognition accuracies of 95.33%, 92.59%, and 74.86% on these datasets, respectively.
Visualizations of the results confirmed that DFL helped the models focus on key characteristics of pests, allowing them to maintain high accuracy even when the distribution of test data changed.
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