Neural Network Training Mitigating Catastrophic Forgetting

The method performs preliminary action by generating synthetic training data for new classes before actual training occurs. The GAN generates images that represent new classes in advance, allowing the network to prepare for learning new concepts without immediately overwriting existing knowledge. This proactive data preparation helps mitigate catastrophic forgetting by establishing a foundation for incremental learning.

The GAN creates copies or synthetic representations of new class data through generated images. Instead of requiring actual new training data that might conflict with existing knowledge, the system creates artificial copies that capture the essential features of new classes. These synthetic copies serve as safe training material that can be integrated without causing catastrophic forgetting of original classes.

The GAN acts as an intermediary between new class concepts and the existing neural network. Instead of directly training the network with new data that causes information loss, the GAN generates intermediate synthetic data that bridges the gap. This intermediary layer allows incremental learning to proceed while protecting previously learned information from being overwritten or forgotten.

The method changes the parameters or characteristics of training data by generating synthetic images with controlled features. Rather than using raw new class data that might cause catastrophic forgetting, the system transforms the data into a suitable form through GAN generation. This parameter transformation allows the network to learn new classes while maintaining stability in previously learned representations.

The system performs self-service by automatically generating its own training data through the GAN. Instead of requiring manual preparation of new class data or complex external interventions to prevent catastrophic forgetting, the network serves itself by creating appropriate training examples. This self-generated data approach maintains training simplicity while improving reliability across all classes.

The method implements feedback by using the neural network's own performance and feature extractions to guide the GAN data generation process. The network's feature extractor provides feedback about what characteristics are important, and the GAN uses this feedback to generate appropriate synthetic data. This closed-loop feedback mechanism maintains simplicity while preventing catastrophic forgetting through intelligent data synthesis.