GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

TL;DR

This paper introduces an analytic framework for visualizing and understanding GANs at multiple levels, revealing how internal units relate to objects and scenes, and enabling targeted improvements and manipulations.

Contribution

It presents a novel visualization and analysis method for GANs, including identifying interpretable units, measuring their causal effects, and enabling practical applications like artifact removal and scene manipulation.

Findings

Identified interpretable units related to object concepts.

Measured causal effects of units on generated objects.

Enabled interactive manipulation of scene elements.

Abstract

Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications, and many GAN variants have emerged with improvements in sample quality and training stability. However, they have not been well visualized or understood. How does a GAN represent our visual world internally? What causes the artifacts in GAN results? How do architectural choices affect GAN learning? Answering such questions could enable us to develop new insights and better models. In this work, we present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level. We first identify a group of interpretable units that are closely related to object concepts using a segmentation-based network dissection method. Then, we quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output. We examine the contextual relationship between these units and their surroundings by inserting the discovered object concepts into new images. We show several practical applications enabled by our framework, from comparing internal representations across different layers, models, and datasets, to improving GANs by locating and removing artifact-causing units, to interactively manipulating objects in a scene. We provide open source interpretation tools to help researchers and practitioners better understand their GAN models.