Landscape Learning for Neural Network Inversion

TL;DR

This paper introduces a learning-based approach to optimize the loss landscape for neural network inversion, significantly improving the efficiency and stability of inverse problem solutions in vision, robotics, and graphics.

Contribution

It proposes a novel method to learn a loss landscape that enables faster and more stable gradient descent for neural network inversion tasks.

Findings

Enhanced inversion speed across multiple tasks

Improved stability of the optimization process

Applicable to generative and discriminative models

Abstract

Many machine learning methods operate by inverting a neural network at inference time, which has become a popular technique for solving inverse problems in computer vision, robotics, and graphics. However, these methods often involve gradient descent through a highly non-convex loss landscape, causing the optimization process to be unstable and slow. We introduce a method that learns a loss landscape where gradient descent is efficient, bringing massive improvement and acceleration to the inversion process. We demonstrate this advantage on a number of methods for both generative and discriminative tasks, including GAN inversion, adversarial defense, and 3D human pose reconstruction.