Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

TL;DR

This paper introduces an unsupervised pixel-level domain adaptation method using GANs, transforming source images to resemble target domain images, improving generalization and outperforming previous approaches.

Contribution

The work presents a novel GAN-based approach for pixel-level domain adaptation that effectively adapts images without supervision, surpassing existing methods in performance.

Findings

Outperforms state-of-the-art in unsupervised domain adaptation

Generates plausible, target-like images from source data

Generalizes to unseen object classes during training

Abstract

Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks. One appealing alternative is rendering synthetic data where ground-truth annotations are generated automatically. Unfortunately, models trained purely on rendered images often fail to generalize to real images. To address this shortcoming, prior work introduced unsupervised domain adaptation algorithms that attempt to map representations between the two domains or learn to extract features that are domain-invariant. In this work, we present a new approach that learns, in an unsupervised manner, a transformation in the pixel space from one domain to the other. Our generative adversarial network (GAN)-based method adapts source-domain images to appear as if drawn from the target domain. Our approach not only produces plausible samples, but also outperforms the state-of-the-art on a number of unsupervised domain adaptation scenarios by large margins. Finally, we demonstrate that the adaptation process generalizes to object classes unseen during training.