RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels
This addresses the critical issue of label noise in few-shot learning for image classification, which is incremental but offers strong robustness gains.
The paper tackles the problem of few-shot image classification with noisy labels by proposing RoNFA, a robust neural field-based approach that uses two neural fields for feature and category representation with adaptive receptive fields, achieving significant accuracy improvements over state-of-the-art methods on real-world datasets with various label noise types, even surpassing clean-set results.
In few-shot learning (FSL), the labeled samples are scarce. Thus, label errors can significantly reduce classification accuracy. Since label errors are inevitable in realistic learning tasks, improving the robustness of the model in the presence of label errors is critical. This paper proposes a new robust neural field-based image approach (RoNFA) for few-shot image classification with noisy labels. RoNFA consists of two neural fields for feature and category representation. They correspond to the feature space and category set. Each neuron in the field for category representation (FCR) has a receptive field (RF) on the field for feature representation (FFR) centered at the representative neuron for its category generated by soft clustering. In the prediction stage, the range of these receptive fields adapts according to the neuronal activation in FCR to ensure prediction accuracy. These learning strategies provide the proposed model with excellent few-shot learning capability and strong robustness against label noises. The experimental results on real-world FSL datasets with three different types of label noise demonstrate that the proposed method significantly outperforms state-of-the-art FSL methods. Its accuracy obtained in the presence of noisy labels even surpasses the results obtained by state-of-the-art FSL methods trained on clean support sets, indicating its strong robustness against noisy labels.