Standard Transformer

Standard Transformers are a fundamental deep learning architecture achieving state-of-the-art results across diverse tasks, from natural language processing to computer vision and even 3D point cloud analysis. Current research focuses on improving efficiency through architectural simplifications, exploring alternative attention mechanisms (e.g., ReLU and addition-based), and leveraging pre-training strategies like masked autoencoders, particularly for applications with limited data. These advancements aim to enhance both the speed and performance of Transformers, broadening their applicability in resource-constrained environments and specialized domains while also highlighting the importance of data preprocessing techniques.