Frequency Feature

Frequency features, encompassing both low and high-frequency components of data, are crucial for various machine learning tasks, with research focusing on effectively extracting, utilizing, and mitigating biases related to these features. Current efforts involve developing novel architectures like frequency-adaptive multi-scale deep neural networks and transformers, often incorporating techniques such as frequency decomposition, attention mechanisms, and adaptive filtering to enhance model performance and robustness. This research is significant because improved handling of frequency features leads to more accurate and reliable results in diverse applications, including image processing, time series forecasting, and audio analysis.