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HandsOff: Labeled Dataset Generation With No Additional Human Annotations

Austin Xu · Mariya I. Vasileva · Achal Dave · Arjun Seshadri

West Building Exhibit Halls ABC 370
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Recent work leverages the expressive power of genera- tive adversarial networks (GANs) to generate labeled syn- thetic 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 in- troduce the HandsOff framework, a technique capable of producing an unlimited number of synthetic images and cor- responding labels after being trained on less than 50 pre- existing labeled images. Our framework avoids the practi- cal drawbacks of prior work by unifying the field of GAN in- version 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 es- timation compared to prior dataset generation approaches and transfer learning baselines. We additionally showcase its ability to address broad challenges in model develop- ment which stem from fixed, hand-annotated datasets, such as the long-tail problem in semantic segmentation. Project page:

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