Reconstruction Loss

Reconstruction loss is a way to measure how accurately a model can reproduce or recreate its original input data. Think of it like copying a drawing: the closer the copy matches the original, the lower the reconstruction loss. In machine learning, this metric helps evaluate how well a model has learned to understand and replicate the data. For example, if an autoencoder compresses images, the reconstruction loss tells us how similar the output image is to the original. A lower loss indicates a better reconstruction, meaning the model has captured the key features of the data effectively.