Analyzing the Robustness of Compact Neural Networks in Noisy Environments

Compact neural networks are becoming increasingly popular because they operate effectively and need less computer power. They still struggle in loud situations. We suggest and evaluate three novel techniques to make compact neural networks more noise-resistant: noise-aware training, adversarial relationship-based training and testing, and noise filtering and knowledge distillation. Networks were subjected to controlled noise during noise-aware training to simulate noisy environments. Data shows that this methodology enhances accuracy, precision, and F1 scores significantly above typical training approaches. Adversarial Training and Evaluation employs adversarial instances. That makes the network more resistant to attacks from other networks. This method may make adversarial-vulnerable systems safer and more robust. Noise filtering and information distillation combine data preparation with information from larger, more reliable models. The two-step approach enhances accuracy while maintaining competitiveness and F1 scores. Our comparative study examines the benefits and downsides of each method to help you pick the best one for your application. Small neural networks are more robust using the provided strategies. Their data-driven responses to noise, bad actor assaults, and data faults make tiny neural networks more trustworthy in hectic real-world circumstances. The improvement of AI security and performance is facilitated by the usage of alternate data formats.