attention layers
Attention layers refer to a type of neural network architecture that allows the system to selectively focus on specific parts of input data by assigning different levels of importance or attention to different elements. These layers capture relationships and dependencies between different parts of the input data, enabling effective information processing and extraction of relevant features during various tasks, such as machine translation or image recognition.
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Related Concepts (21)
- attention in computer vision
- attention in graph neural networks
- attention in machine translation
- attention mechanisms
- attention-based audio processing
- attention-based recommendation systems
- attention-based sequence-to-sequence models
- deep learning models
- generative adversarial networks with attention
- hierarchical attention networks
- memory attention networks
- multi-head attention
- natural language processing (nlp)
- neural network architectures
- neural network layers
- recurrent neural networks with attention
- reinforcement learning with attention
- self-attention
- sentiment analysis with attention layers
- transformer models
- visual attention