Learnable Wavelet

Learnable wavelets are a burgeoning research area focusing on integrating the power of wavelet transforms into deep learning models, primarily to improve feature extraction and representation learning in various applications. Current research emphasizes incorporating learnable wavelet modules into existing architectures like convolutional neural networks and transformers, often within multi-scale frameworks or for specific tasks such as time series classification, image deblurring, and cosmological inference. This approach aims to leverage wavelets' ability to capture both spatial and frequency information efficiently, leading to improved accuracy and interpretability compared to traditional methods, particularly when dealing with limited data or complex data structures. The resulting models show promise for advancing diverse fields, from medical image analysis to scientific computing.