ResNet Based

ResNet-based architectures are a cornerstone of deep learning, primarily used for image classification and related tasks, but increasingly applied to diverse areas like speech recognition and medical image analysis. Current research focuses on improving ResNet's efficiency and robustness through modifications such as incorporating attention mechanisms, specialized convolutional units (e.g., PushPull-Conv), and integrating them with other architectures like transformers. These advancements enhance accuracy, reduce computational costs, and improve model interpretability, leading to significant impacts across various fields including healthcare, remote sensing, and robotics.