Hidden Layer

Hidden layers are the intermediate processing stages within artificial neural networks, crucial for learning complex patterns from data. Current research focuses on understanding their role in model performance, interpretability, and robustness, exploring architectures like those employing Chebyshev functions or quantization-aware training for efficiency. Investigations into hidden layer activations, including their linear separability and norm characteristics, aim to improve model explainability and address vulnerabilities to adversarial attacks. These efforts are significant for advancing both theoretical understanding of deep learning and practical applications, particularly in areas demanding high accuracy, efficiency, and trustworthiness.