Transformer Based Network

Transformer-based networks are a class of deep learning models achieving state-of-the-art results across diverse applications by leveraging self-attention mechanisms to capture long-range dependencies within data. Current research focuses on improving efficiency (e.g., through pruning, lightweight architectures, and optimized attention mechanisms), enhancing explainability, and adapting transformers to specific data modalities (e.g., images, point clouds, time series). These advancements are significantly impacting fields like computer vision, natural language processing, and medical image analysis, leading to improved accuracy and efficiency in tasks ranging from object detection to medical diagnosis.