A multi-head decoder is a fundamental building block within sequence-to-sequence models, most notably the Transformer architecture, responsible for generating an output sequence based on an encoded input and its own preceding outputs. The "multi-head" aspect refers to its use of multiple parallel self-attention mechanisms. Each "head" independently learns to attend to different parts of the input and previously generated sequence, extracting distinct types of information or focusing on various relational aspects. The outputs from these heads are then concatenated and linearly transformed, allowing the decoder to integrate diverse perspectives to produce the next output token. This parallel processing significantly enhances the model's ability to capture complex, long-range dependencies and semantic relationships, leading to more coherent and contextually relevant outputs. It is widely used by researchers and ML engineers in natural language processing (e.g., machine translation), computer vision (e.g., image captioning), and multimodal AI, such as the emotional talking face synthesis mentioned in the abstract.
Multi-head decoders are key components in advanced AI models, especially those that generate sequences like text or images. They work by using multiple "attention" mechanisms in parallel to focus on different parts of the input data simultaneously, helping the AI understand complex relationships and produce more accurate and relevant outputs.
Multi-head attention (MHA), Transformer decoder, Decoder block
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