Linear Attention Transformer
Linear Attention Transformers aim to improve the efficiency of standard Transformer models by reducing the computational complexity of the attention mechanism from quadratic to linear time. Current research focuses on enhancing the performance of these linear attention models, particularly through incorporating gating mechanisms (like Gated Linear Attention), modifying architectural designs (e.g., Mamba-like architectures), and exploring their application in various domains including language modeling, image generation, and speech recognition. These advancements offer significant potential for deploying large-scale Transformer models on resource-constrained devices and accelerating training and inference speeds, thereby impacting both research and practical applications.