Dominant Low Frequency

Dominant low-frequency analysis focuses on identifying and leveraging the most influential low-frequency components within various data types, aiming to improve prediction accuracy, enhance model robustness, and gain deeper insights into underlying data structures. Current research explores frequency-domain transformations and novel neural network architectures, such as sinusoidal MLPs and frequency-specific convolutional networks, to effectively capture and utilize these dominant frequencies. This approach shows promise in diverse applications, including time-series forecasting, deepfake detection, and mitigating adversarial attacks in machine learning, by improving model performance and generalization capabilities.