Neural Network Layer Selection Under Fine-Tuning Memory Constraints

The patent segments the neural network into multiple layers and identifies only the critical layers that contribute most to performance deviations. By applying Fisher information analysis, the system determines which specific layers require fine-tuning rather than retraining the entire network. This segmentation approach reduces the scope of fine-tuning operations, thereby lowering memory consumption and energy expenditure while maintaining precision improvement.

The patent applies local quality by focusing fine-tuning resources on specific critical layers rather than uniformly processing all layers. The Fisher information analysis identifies layers with highest impact on output deviations, and fine-tuning is concentrated on these localized regions. This selective approach optimizes the allocation of computational resources, reducing overall energy consumption while achieving the desired precision improvement.

The patent divides the neural network into distinct layers and uses Fisher information analysis to identify which segments (layers) are most responsible for performance deviations. By isolating and fine-tuning only these critical segments rather than the entire network, the memory occupation is significantly reduced while still achieving adequate adaptation to the deployment environment.

The patent applies partial action by performing fine-tuning on only a subset of layers identified as critical through Fisher information analysis, rather than applying full retraining to all layers. This partial fine-tuning approach provides sufficient adaptation to deployment data while consuming less memory, as evidenced by the comparison between full retraining memory requirements and the reduced memory usage of selective fine-tuning.