RESEARCH

COMPUTER VISION

Understanding Deep Networks via Extremal Perturbations and Smooth Masks

October 20, 2019

Abstract

The problem of attribution is concerned with identifying the parts of an input that are responsible for a model's output. An important family of attribution methods is based on measuring the effect of perturbations applied to the input. In this paper, we discuss some of the shortcomings of existing approaches to perturbation analysis and address them by introducing the concept of extremal perturbations, which are theoretically grounded and interpretable. We also introduce a number of technical innovations to compute extremal perturbations, including a new area constraint and a parametric family of smooth perturbations, which allow us to remove all tunable hyper-parameters from the optimization problem. We analyze the effect of perturbations as a function of their area, demonstrating excellent sensitivity to the spatial properties of the deep neural network under stimulation. We also extend perturbation analysis to the intermediate layers of a network. This application allows us to identify the salient channels necessary for classification, which, when visualized using feature inversion, can be used to elucidate model behavior. Lastly, we introduce TorchRay, an interpretability library built on PyTorch.

Download the Paper

AUTHORS

Written by

Andrea Vedaldi

Ruth Fong

Mandela Patrick

Publisher

ICCV

Research Topics

Computer Vision

Related Publications

July 02, 2024

GRAPHICS

COMPUTER VISION

Meta 3D AssetGen: Text-to-Mesh Generation with High-Quality Geometry, Texture, and PBR Materials

Yawar Siddiqui, Tom Monnier, Filippos Kokkinos, Mahendra Kariya, Yanir Kleiman, Emilien Garreau, Oran Gafni, Natalia Neverova, Andrea Vedaldi, Roman Shapovalov, David Novotny

July 02, 2024

July 02, 2024

GRAPHICS

COMPUTER VISION

Meta 3D Gen

Raphael Bensadoun, Tom Monnier, Yanir Kleiman, Filippos Kokkinos, Yawar Siddiqui, Mahendra Kariya, Omri Harosh, Roman Shapovalov, Emilien Garreau, Animesh Karnewar, Ang Cao, Idan Azuri, Iurii Makarov, Eric-Tuan Le, Antoine Toisoul, David Novotny, Oran Gafni, Natalia Neverova, Andrea Vedaldi

July 02, 2024

July 02, 2024

GRAPHICS

COMPUTER VISION

Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D Objects

Raphael Bensadoun, Yanir Kleiman, Idan Azuri, Omri Harosh, Andrea Vedaldi, Natalia Neverova, Oran Gafni

July 02, 2024

June 20, 2024

COMPUTER VISION

ICON: Incremental CONfidence for Joint Pose and Radiance Field Optimization

Weiyao Wang, Pierre Gleize, Hao Tang, Xingyu Chen, Kevin Liang, Matt Feiszli

June 20, 2024

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.