Complexity of Representations in Deep Learning
This work provides incremental insights into understanding deep learning representations for researchers, focusing on data complexity changes.
The authors analyzed how data complexity evolves through deep neural network layers during training, using a simple complexity measure and a popular benchmarking task, and found that network design and training sample availability impact representation effectiveness.
Deep neural networks use multiple layers of functions to map an object represented by an input vector progressively to different representations, and with sufficient training, eventually to a single score for each class that is the output of the final decision function. Ideally, in this output space, the objects of different classes achieve maximum separation. Motivated by the need to better understand the inner working of a deep neural network, we analyze the effectiveness of the learned representations in separating the classes from a data complexity perspective. Using a simple complexity measure, a popular benchmarking task, and a well-known architecture design, we show how the data complexity evolves through the network, how it changes during training, and how it is impacted by the network design and the availability of training samples. We discuss the implications of the observations and the potentials for further studies.