Pre-training Apparatus for Image Target Tasks

The patent creates multiple copies and variations of limited labeled images through affine transformations (rotation, scaling, translation) and other image processing techniques. This generates synthetic training data without requiring additional physical measurements, thereby maintaining high training accuracy while avoiding increased measurement costs.

The patent performs pre-training using synthetically generated images before fine-tuning with actual labeled data. This preliminary training phase prepares the neural network with basic image recognition capabilities using low-cost synthetic data, reducing the need for large amounts of expensive labeled measurement data in subsequent training stages.

The patent employs universal image transformation techniques (affine transformations, color space conversions, geometric modifications) that can be applied to any type of image data regardless of the specific task domain. This creates a general-purpose pre-training methodology that adapts to different task types without requiring task-specific labeled data, enhancing both performance and versatility.

The patent systematically varies image parameters (rotation angles, scaling factors, translation distances, color channel combinations) to generate diverse training samples from limited original images. This parameter-based approach creates task-agnostic training data that maintains effectiveness across different application domains while requiring minimal original data.