LARGE: Latent-Based Regression through GAN Semantics

TL;DR

This paper introduces a method that uses the semantic structure of GAN latent spaces to perform regression tasks with minimal supervision, enabling effective predictions and attribute sorting across diverse domains.

Contribution

The authors demonstrate that linear directions in GAN latent spaces can be exploited for regression with as few as two labeled samples, advancing few-shot learning techniques.

Findings

Achieves state-of-the-art results in few-shot regression tasks.

Works across multiple domains and GAN frameworks.

Enables attribute-based image sorting without explicit supervision.

Abstract

We propose a novel method for solving regression tasks using few-shot or weak supervision. At the core of our method is the fundamental observation that GANs are incredibly successful at encoding semantic information within their latent space, even in a completely unsupervised setting. For modern generative frameworks, this semantic encoding manifests as smooth, linear directions which affect image attributes in a disentangled manner. These directions have been widely used in GAN-based image editing. We show that such directions are not only linear, but that the magnitude of change induced on the respective attribute is approximately linear with respect to the distance traveled along them. By leveraging this observation, our method turns a pre-trained GAN into a regression model, using as few as two labeled samples. This enables solving regression tasks on datasets and attributes which are difficult to produce quality supervision for. Additionally, we show that the same latent-distances can be used to sort collections of images by the strength of given attributes, even in the absence of explicit supervision. Extensive experimental evaluations demonstrate that our method can be applied across a wide range of domains, leverage multiple latent direction discovery frameworks, and achieve state-of-the-art results in few-shot and low-supervision settings, even when compared to methods designed to tackle a single task.