Transformer Based Structure

Transformer-based structures are revolutionizing various fields by leveraging attention mechanisms to model long-range dependencies in data. Current research focuses on improving efficiency (e.g., through token reduction and compact architectures like Mixers), addressing limitations such as oversmoothing and adversarial vulnerability, and adapting transformers for specific tasks (e.g., audio classification, image inpainting, and industrial prognostics). These advancements are significantly impacting diverse applications, from medical imaging and natural language processing to resource-constrained settings, by enhancing model accuracy, robustness, and efficiency.