HandsOff: Labeled Dataset Generation With No Additional Human Annotations
This addresses the practical challenge for practitioners in computer vision who need labeled datasets but face costs and quality issues from manual annotation, offering a novel solution that is not incremental.
The paper tackles the problem of generating labeled synthetic datasets without additional human annotations by introducing the HandsOff framework, which achieves state-of-the-art performance in tasks like semantic segmentation, keypoint detection, and depth estimation across multiple domains.
Recent work leverages the expressive power of generative adversarial networks (GANs) to generate labeled synthetic datasets. These dataset generation methods often require new annotations of synthetic images, which forces practitioners to seek out annotators, curate a set of synthetic images, and ensure the quality of generated labels. We introduce the HandsOff framework, a technique capable of producing an unlimited number of synthetic images and corresponding labels after being trained on less than 50 pre-existing labeled images. Our framework avoids the practical drawbacks of prior work by unifying the field of GAN inversion with dataset generation. We generate datasets with rich pixel-wise labels in multiple challenging domains such as faces, cars, full-body human poses, and urban driving scenes. Our method achieves state-of-the-art performance in semantic segmentation, keypoint detection, and depth estimation compared to prior dataset generation approaches and transfer learning baselines. We additionally showcase its ability to address broad challenges in model development which stem from fixed, hand-annotated datasets, such as the long-tail problem in semantic segmentation. Project page: austinxu87.github.io/handsoff.