Soft Thresholding

Soft thresholding is a technique used to sparsify data by shrinking small values to zero, enhancing signal-to-noise ratios and improving model efficiency. Current research focuses on integrating soft thresholding into various neural network architectures, including unfolded iterative algorithms like FISTA and ADMM, and convolutional neural networks, often with adaptive or channel-wise thresholding mechanisms to optimize performance for specific tasks. This approach finds applications in diverse fields such as image processing (denoising, reconstruction, segmentation), compressive sensing, and multitask learning, leading to improved model accuracy, reduced computational cost, and enhanced robustness to noise and domain shifts.