# Diagnosing Convolutional Neural Networks using their Spectral Response

arXiv: https://arxiv.org/abs/1810.03241 · HTML: https://mlsanity.com/paper/1810.03241/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization](https://mlsanity.com/paper/2602.23630/) (2026)

## Abstract (arXiv)

Convolutional Neural Networks (CNNs) are a class of artificial neural networks whose computational blocks use convolution, together with other linear and non-linear operations, to perform classification or regression. This paper explores the spectral response of CNNs and its potential use in diagnosing problems with their training. We measure the gain of CNNs trained for image classification on ImageNet and observe that the best models are also the most sensitive to perturbations of their input. Further, we perform experiments on MNIST and CIFAR-10 to find that the gain rises as the network learns and then saturates as the network converges. Moreover, we find that strong gain fluctuations can point to overfitting and learning problems caused by a poor choice of learning rate. We argue that the gain of CNNs can act as a diagnostic tool and potential replacement for the validation loss when hold-out validation data are not available.