Transformer Baseline

Transformer baselines are foundational models serving as benchmarks for evaluating advancements in various deep learning applications, from natural language processing to computer vision and speech translation. Current research focuses on improving efficiency and performance through architectural modifications like linear attention mechanisms, depth-weighted averaging, and pyramid structures, as well as incorporating memory augmentation and dynamic compression techniques. These improvements aim to address limitations such as quadratic complexity and long-range dependency modeling, leading to more efficient and effective models for diverse tasks. The resulting optimized baselines are crucial for accelerating progress and enabling wider deployment of transformer-based technologies.