# Network Dissection: Quantifying Interpretability of Deep Visual Representations

## TL;DR

Network Dissection introduces a framework to quantify and analyze the interpretability of CNN hidden units by evaluating their alignment with semantic concepts across various network architectures and training conditions.

## Contribution

The paper presents a novel method for measuring interpretability of CNN representations and applies it to compare models, training methods, and architectural choices.

## Key findings

- Interpretability varies with training iterations and network depth.
- Certain training techniques improve the semantic alignment of units.
- Interpretability provides insights beyond discriminative performance.

## Abstract

We propose a general framework called Network Dissection for quantifying the interpretability of latent representations of CNNs by evaluating the alignment between individual hidden units and a set of semantic concepts. Given any CNN model, the proposed method draws on a broad data set of visual concepts to score the semantics of hidden units at each intermediate convolutional layer. The units with semantics are given labels across a range of objects, parts, scenes, textures, materials, and colors. We use the proposed method to test the hypothesis that interpretability of units is equivalent to random linear combinations of units, then we apply our method to compare the latent representations of various networks when trained to solve different supervised and self-supervised training tasks. We further analyze the effect of training iterations, compare networks trained with different initializations, examine the impact of network depth and width, and measure the effect of dropout and batch normalization on the interpretability of deep visual representations. We demonstrate that the proposed method can shed light on characteristics of CNN models and training methods that go beyond measurements of their discriminative power.