Fourier Attention

Fourier attention leverages Fast Fourier Transforms (FFTs) to enhance attention mechanisms in neural networks, primarily aiming to improve computational efficiency and the capture of global patterns in data, particularly for long sequences or high-resolution images. Current research focuses on integrating Fourier attention into various architectures, including transformers and convolutional networks, for tasks such as time-series forecasting, crowd counting, and image recognition, often comparing its performance against traditional attention methods. This approach offers significant potential for accelerating inference speeds and improving accuracy in diverse applications, particularly those involving large datasets or complex patterns.