Few-Shot Image Recognition via Neural Network Weight Transfer

The patent applies preliminary action by pre-training a baseline neural network on abundant many-shot class data before using it to generate synthetic few-shot training data. This preliminary training establishes a foundation that can be leveraged to create effective training sets for classes with limited actual samples, thereby improving pattern recognition effectiveness without requiring extensive collection of balanced training data across all classes.

The patent employs copying by using the baseline neural network to generate synthetic training images for few-shot classes. Instead of collecting real images for every class (which would be time-consuming), the system copies and adapts features from the pre-trained baseline network to create artificial training data that mimics the appearance and characteristics of the target few-shot classes, thus achieving effective training with minimal actual sample collection.

The patent applies segmentation by dividing the training process into distinct stages: first training a baseline network on many-shot classes, then using that baseline to generate prototypes for few-shot classes, and finally fine-tuning with a small number of actual few-shot images. This segmentation allows each stage to focus on specific learning objectives, improving classification accuracy while managing complexity through structured progression rather than attempting to train all classes simultaneously.

The baseline neural network serves as an intermediary between the abundant many-shot data and the scarce few-shot data. It processes and transforms the many-shot training experience into prototype representations that can guide the training of few-shot class classifiers. This intermediary role allows knowledge transfer across data scarcity boundaries, improving few-shot classification accuracy without requiring direct access to large amounts of few-shot training data.