Pre Trained Deep Neural Network

Pre-trained deep neural networks (DNNs) leverage massive datasets to learn generalizable feature representations, significantly accelerating downstream task training and reducing computational demands. Current research focuses on improving their fairness, efficiency (including backpropagation-free training and pruning techniques), and out-of-distribution generalization, often employing techniques like dropout, gradient boosting, and evolutionary algorithms to optimize model architectures and training processes. These advancements are crucial for deploying DNNs in resource-constrained environments and mitigating biases, thereby enhancing the reliability and applicability of deep learning across diverse scientific and practical domains.