# Representational Distance Learning for Deep Neural Networks

arXiv: https://arxiv.org/abs/1511.03979 · HTML: https://mlsanity.com/paper/1511.03979/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2015.

## Built on by (most cited first, 5 of 5)

- [Architecture Agnostic Federated Learning for Neural Networks](https://mlsanity.com/paper/2202.07757/) (2022)
- [Towards robust vision by multi-task learning on monkey visual cortex](https://mlsanity.com/paper/2107.14344/) (2021)
- [From superposition to sparse codes: interpretable representations in neural networks](https://mlsanity.com/paper/2503.01824/) (2025)
- [Exploring the Similarity of Representations in Model-Agnostic Meta-Learning](https://mlsanity.com/paper/2105.05757/) (2021)
- [HARD: Hard Augmentations for Robust Distillation](https://mlsanity.com/paper/2305.14890/) (2023)

## Abstract (arXiv)

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.