Patch Wise Mask

Patch-wise masking is a technique used in various computer vision and machine learning tasks to improve model robustness and performance. Current research focuses on integrating masking into different model architectures, such as transformers and convolutional neural networks, often employing it as a data augmentation strategy or for self-supervised learning. This approach is proving valuable in diverse applications, including shadow removal, 3D pose estimation, and medical image generation, by enhancing model generalization and mitigating issues like overfitting and data scarcity. The effectiveness of patch-wise masking highlights its potential to become a standard technique for improving the performance and reliability of various machine learning models.