Class Interference of Deep Neural Networks

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. Deep neural networks are very successful for classification (LeCun et al., 2015; Goodfellow et al., 2016) and sequential decision making (Mnih et al., 2015; Silver et al., 2016). However, there lacks a good understanding of why they work well and where is the bottleneck. For example, it is well known that larger learning rates and smaller batch sizes can train models that generalize better.