FCANone

FCANone is a training technique that focuses on improving neural network performance by encouraging the model to learn more robust features. It does this by minimizing unwanted correlations in the learned representations, helping the model generalize better to new data. Essentially, FCANone aims to prevent the model from relying on spurious or irrelevant patterns, leading to more accurate and reliable predictions across diverse situations.