OCW: Enhancing Few-Shot Learning with Optimized Class-Weighting Methods

Few-shot learning, the capability of a machine learning model to comprehend and adapt to new classes with limited instances, has been a critical area of research in the realm of artificial intelligence. To effectively train a model on a new class, a sufficient quantity of diverse samples is required. However, in the case of datasets with low similarity among classes, there is a limitation in the availability of samples for each specific class. This scarcity or imbalance of samples makes it challenging for the model to generalize effectively. This study introduces an innovative weight optimization technique, the Optimized Class-Weighting (OCW) method, designed to improve model accuracy in these contexts. The OCW method, born from the necessity to ameliorate the challenges of few-shot learning, optimizes the allocation of weights to different classes in a dataset, thereby enhancing the model's discriminatory power. Our research systematically evaluates the efficacy of the OCW method across four state-of-the-art models - MobileNetv2, Vision Transformer (ViT), ResNet50, and EfficientNet - under various shot scenarios, from 1-shot to 5-shot cases. Our findings demonstrate a consistent improvement in model performance upon the application of the OCW method across all models and shot scenarios. For instance, the MobileNetv2 model exhibited a substantial improvement in the 1-shot scenario, with accuracy increasing from 66 % to 85% upon the implementation of the OCW method. This study underscores the robustness of the OCW method in augmenting model performance in few-shot learning, providing a promising avenue for future research in this field. Our work contributes significantly to the ongoing discourse on weight optimization techniques, setting the stage for further enhancements in the performance of AI models.