Recognizing and telling similar objects apart is even hard for human beings.
In this paper, we show that there is a phenomenon of class interference with
all deep neural networks. Class interference represents the learning difficulty
in data, and it constitutes the largest percentage of generalization errors by
deep networks. To understand class interference, we propose cross-class tests,
class ego directions and interference models. We show how to use these
definitions to study minima flatness and class interference of a trained model.
We also show how to detect class interference during training through label
dancing pattern and class dancing notes.