👉 Fixtures computing is a computational technique used in machine learning and deep learning where a dataset, often referred to as a "fixed" or "fixed-point" dataset, is preprocessed and augmented to create a diverse set of training examples while maintaining the original data distribution. This approach involves applying transformations such as rotations, translations, scaling, and flipping to the fixed dataset, generating new samples that are semantically similar but not identical to the originals. These transformed examples are then used to train neural networks, helping them generalize better by exposing the model to a wider variety of input patterns. By leveraging fixtures, researchers and practitioners can improve model robustness and performance without the need for extensive and costly data collection or labeling processes.
