# Diagnostic Visualization for Deep Neural Networks Using Stochastic Gradient Langevin Dynamics

## TL;DR

This paper introduces LDAM, a stochastic gradient-based method for visualizing and interpreting deep neural network activations, enabling exploration of multiple solutions and balancing interpretability with pixel accuracy.

## Contribution

The paper proposes LDAM, a novel MCMC-based diagnostic visualization technique that combines exploration of activation maximizers with a GAN-style regularizer for interpretability.

## Key findings

- LDAM effectively explores multiple activation maximizers.
- It balances interpretability and pixel-level accuracy.
- Provides insights into parameter averaging in deep training.

## Abstract

The internal states of most deep neural networks are difficult to interpret, which makes diagnosis and debugging during training challenging. Activation maximization methods are widely used, but lead to multiple optima and are hard to interpret (appear noise-like) for complex neurons. Image-based methods use maximally-activating image regions which are easier to interpret, but do not provide pixel-level insight into why the neuron responds to them. In this work we introduce an MCMC method: Langevin Dynamics Activation Maximization (LDAM), which is designed for diagnostic visualization. LDAM provides two affordances in combination: the ability to explore the set of maximally activating pre-images, and the ability to trade-off interpretability and pixel-level accuracy using a GAN-style discriminator as a regularizer. We present case studies on MNIST, CIFAR and ImageNet datasets exploring these trade-offs. Finally we show that diagnostic visualization using LDAM leads to a novel insight into the parameter averaging method for deep net training.