What are the types of attention mechanisms?
The main types of attention mechanisms include Soft vs. Hard Attention, Global vs. Local Attention, Self-Attention vs. Vanilla Attention, and Additive vs. Dot-Product Attention mechanisms.
Each type serves distinct purposes. Soft Attention calculates a weighted distribution over all input elements, while Hard Attention selects a single element. Global Attention considers all elements, whereas Local Attention focuses on a subset. Self-Attention, vital for models like Transformers, relates different positions within a single sequence; Vanilla Attention typically applies between different sequences like encoder-decoder. Additive (e.g., Bahdanau) uses a learned feed-forward network for scoring, and Dot-Product computes alignment scores directly via vector dot products, offering computational efficiency.
These mechanisms enhance modeling capabilities. Self-Attention captures long-range dependencies within data sequences, driving breakthroughs in machine translation and text generation. Local Attention balances performance and computational cost. Dot-product attention scales efficiently. They are fundamental in state-of-the-art architectures, enabling superior performance in NLP, computer vision, and multimodal AI applications.
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