Standard Deep

Standard deep learning focuses on improving the accuracy, efficiency, and robustness of artificial neural networks for various tasks, including image classification, time series prediction, and speech recognition. Current research emphasizes enhancing model architectures (like ResNets, Transformers, and Capsule Networks) through techniques such as wavelet decomposition for multiresolution analysis, heterogeneous memory augmentation for improved data efficiency, and novel regularization methods to mitigate spectral bias. These advancements aim to address limitations in generalization, explainability, and adversarial vulnerability, ultimately leading to more reliable and efficient deep learning models with broader applicability.