Enhancing Few-Shot Learning in Lightweight Models via Dual-Faceted Knowledge Distillation

Enhancing Few-Shot Learning in Lightweight Models via Dual-Faceted Knowledge Distillation

Blog Article

In recent computer vision research, the pursuit of improved classification performance often leads to the adoption of complex, large-scale models.However, the actual deployment of such extensive models poses significant challenges in environments constrained by limited computing viqua-f4 power and storage capacity.Consequently, this study is dedicated to addressing these challenges by focusing on innovative methods that enhance the classification performance of lightweight models.

We propose a novel method to compress the knowledge learned by a large model into a lightweight one so that the latter can also achieve good performance in few-shot classification tasks.Specifically, we propose a dual-faceted knowledge distillation strategy that combines output-based and intermediate feature-based methods.The output-based method concentrates on distilling knowledge related to base class labels, while the intermediate feature-based approach, augmented by feature error distribution calibration, tackles the potential non-Gaussian click here nature of feature deviations, thereby boosting the effectiveness of knowledge transfer.

Experiments conducted on MiniImageNet, CIFAR-FS, and CUB datasets demonstrate the superior performance of our method over state-of-the-art lightweight models, particularly in five-way one-shot and five-way five-shot tasks.