Neural Network Classification for Imbalanced Image Data

The baseline model is trained in advance on many-shot classes to learn general image features and class distributions. This preliminary training enables the prototypical network to focus only on learning class prototypes from few-shot data, significantly reducing the training time required for imbalanced classes while maintaining recognition accuracy.

The training process is segmented into two distinct stages: (1) baseline model training on many-shot classes using standard supervised learning, and (2) prototypical network training on few-shot classes using metric learning. This segmentation allows each stage to optimize for its specific data characteristics, achieving high accuracy without requiring extensive training data for all classes.

The baseline model serves as an intermediary that pre-processes image data by extracting general features and establishing a feature space. This intermediary representation allows the prototypical network to effectively learn class prototypes even with very limited training images per class, as the heavy feature extraction burden has already been addressed by the baseline model.

The approach changes the training parameters from standard cross-entropy loss to metric learning loss (e.g., Euclidean distance or cosine similarity) in the prototypical network. This parameter change enables the system to effectively train on imbalanced data by focusing on learning discriminative distances between class prototypes rather than requiring balanced class distributions.

The classification system is segmented into two specialized networks: a baseline model for general feature extraction and a prototypical network for few-shot classification. This segmentation allows each network to be optimized for its specific function, with the baseline model handling many-shot classes and the prototypical network specializing in few-shot classes, thereby improving overall accuracy without excessive complexity.

The baseline model performs multiple functions: it serves as a feature extractor for both many-shot and few-shot classes, and its learned feature space provides a foundation for the prototypical network. This multi-functionality reduces the need for separate specialized models while maintaining high classification accuracy across imbalanced classes.