# Image Decomposition and Classification through a Generative Model

## TL;DR

This paper introduces a generative model that enhances image classification robustness by decomposing inputs into components, effectively handling adversarial attacks and distribution shifts, demonstrated on multiple datasets.

## Contribution

A conditional variational autoencoder is proposed to simultaneously decompose images and classify components, improving robustness against adversarial and distributional challenges.

## Key findings

- Effective decomposition of overlapping components in multiMNIST.
- High robustness to gradient and non-gradient attacks on MNIST and NORB.
- Successful recognition of novel component combinations in traffic signs.

## Abstract

We demonstrate in this paper that a generative model can be designed to perform classification tasks under challenging settings, including adversarial attacks and input distribution shifts. Specifically, we propose a conditional variational autoencoder that learns both the decomposition of inputs and the distributions of the resulting components. During test, we jointly optimize the latent variables of the generator and the relaxed component labels to find the best match between the given input and the output of the generator. The model demonstrates promising performance at recognizing overlapping components from the multiMNIST dataset, and novel component combinations from a traffic sign dataset. Experiments also show that the proposed model achieves high robustness on MNIST and NORB datasets, in particular for high-strength gradient attacks and non-gradient attacks.