Residual Vector

Residual vectors represent the difference between two vectors, often used in machine learning to capture discrepancies or deviations. Current research focuses on leveraging residual vectors for various tasks, including image enhancement (by guiding networks towards desired image characteristics), analyzing neural network behavior (by visualizing information loss within layers), and improving data representation (e.g., for categorical variables using compact bit vectors). This approach offers benefits such as faster training, improved accuracy, and enhanced interpretability of models, impacting fields like computer vision and data analysis.