Inception Distance

Inception Distance (ID) is a family of metrics used to evaluate the quality of images generated by deep learning models, primarily by comparing the feature distributions of generated and real images. Current research focuses on improving ID's accuracy and efficiency, addressing limitations such as its sensitivity to specific model architectures, reliance on pre-trained networks (like InceptionV3), and susceptibility to biases in training data. These improvements aim to create more reliable and objective evaluation methods for generative models, impacting various fields including medical imaging, video generation, and other areas where realistic synthetic data is crucial.