Self-attention Mechanisms
Self-attention mechanisms are a type of attention mechanism in deep learning that allow a model to weigh the importance of different parts of its own input when producing a single output. This is achieved by computing a weighted sum of the input features, where the weights are learned by the model during training. Self-attention mechanisms have been shown to be particularly effective in natural language processing tasks, such as machine translation and text classification, where the input sequences can be of varying lengths and the relevant information can be spread out across the sequence. They have also been used in computer vision tasks, such as image captioning and object detection, where the model needs to selectively attend to different regions of an image. Self-attention mechanisms can be implemented using various neural network architectures, such as the Transformer and the BERT models.
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