👉 Tahoe math, also known as tahoe regularization or total variation regularization, is a technique used in optimization problems, particularly in image processing and machine learning, to prevent overfitting. It adds a penalty term to the loss function that measures the difference between the original image and its smoothed version, controlled by a regularization parameter. This penalty discourages large changes in pixel values between neighboring pixels, promoting solutions that are smoother and more natural-looking. Unlike L2 regularization, which focuses on minimizing the magnitude of weights, tahoe math directly penalizes spatial discontinuities, making it especially effective for tasks like image denoising, deblurring, and segmentation.
