Residual Encoding is a technique used in data compression and machine learning to represent the difference between actual data and its predicted or approximated version, thereby reducing redundancy and improving efficiency. It is commonly used in neural networks, particularly in architectures like ResNet, to facilitate the training of deep models by allowing gradients to flow more easily through layers.