Representational Distance Learning for Deep Neural Networks
This method addresses the challenge of transfer learning across different architectures, potentially enabling models to replicate biological brain representations, but it is incremental as it builds on existing transfer learning techniques.
The paper tackles the problem of enabling a deep neural network (student) to learn from the internal representational spaces of a reference model (teacher), such as another DNN or a biological brain, by using representational distance matrices (RDMs). The result is that representational distance learning (RDL) is competitive with other transfer learning techniques on MNIST and CIFAR-100 datasets, significantly improving visual classification performance compared to baseline networks without transfer learning.
Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher. We demonstrate that RDL is competitive with other transfer learning techniques for two publicly available benchmark computer vision datasets (MNIST and CIFAR-100), while allowing for architectural differences between student and teacher. By pulling the student's RDMs towards those of the teacher, RDL significantly improved visual classification performance when compared to baseline networks that did not use transfer learning. In the future, RDL may enable combined supervised training of deep neural networks using task constraints (e.g. images and category labels) and constraints from brain-activity measurements, so as to build models that replicate the internal representational spaces of biological brains.