DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort

• URL: http://arxiv.org/abs/2104.06490v1
• Date: Tue, 13 Apr 2021 20:08:29 GMT
• Title: DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort
• Authors: Yuxuan Zhang, Huan Ling, Jun Gao, Kangxue Yin, Jean-Francois Lafleche, Adela Barriuso, Antonio Torralba, Sanja Fidler
• Abstract summary: Current deep networks are extremely data-hungry, benefiting from training on large-scale datasets. We show how the GAN latent code can be decoded to produce a semantic segmentation of the image. These generated datasets can then be used for training any computer vision architecture just as real datasets are.
• Score: 117.41383937100751
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce DatasetGAN: an automatic procedure to generate massive datasets of high-quality semantically segmented images requiring minimal human effort. Current deep networks are extremely data-hungry, benefiting from training on large-scale datasets, which are time consuming to annotate. Our method relies on the power of recent GANs to generate realistic images. We show how the GAN latent code can be decoded to produce a semantic segmentation of the image. Training the decoder only needs a few labeled examples to generalize to the rest of the latent space, resulting in an infinite annotated dataset generator! These generated datasets can then be used for training any computer vision architecture just as real datasets are. As only a few images need to be manually segmented, it becomes possible to annotate images in extreme detail and generate datasets with rich object and part segmentations. To showcase the power of our approach, we generated datasets for 7 image segmentation tasks which include pixel-level labels for 34 human face parts, and 32 car parts. Our approach outperforms all semi-supervised baselines significantly and is on par with fully supervised methods, which in some cases require as much as 100x more annotated data as our method.

Related papers

• UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation [64.01742988773745]
  An increasing privacy concern exists regarding training large-scale image segmentation models on unauthorized private data. We exploit the concept of unlearnable examples to make images unusable to model training by generating and adding unlearnable noise into the original images. We empirically verify the effectiveness of UnSeg across 6 mainstream image segmentation tasks, 10 widely used datasets, and 7 different network architectures.
  arXiv  Detail & Related papers  (2024-10-13T16:34:46Z)
• DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models [61.906934570771256]
  We present a generic dataset generation model that can produce diverse synthetic images and perception annotations. Our method builds upon the pre-trained diffusion model and extends text-guided image synthesis to perception data generation. We show that the rich latent code of the diffusion model can be effectively decoded as accurate perception annotations using a decoder module.
  arXiv  Detail & Related papers  (2023-08-11T14:38:11Z)
• NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Images [1.1500025852056222]
  We release one of the biggest fully manually annotated datasets of nuclei in Hematoxylin and Eosin (H&E)-stained histological images, called NuInsSeg. This dataset contains 665 image patches with more than 30,000 manually segmented nuclei from 31 human and mouse organs. For the first time, we provide additional ambiguous area masks for the entire dataset.
  arXiv  Detail & Related papers  (2023-08-03T13:45:07Z)
• HandsOff: Labeled Dataset Generation With No Additional Human Annotations [13.11411442720668]
  We introduce the HandsOff framework, a technique capable of producing an unlimited number of synthetic images and corresponding labels. 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.
  arXiv  Detail & Related papers  (2022-12-24T03:37:02Z)
• High-Quality Entity Segmentation [110.55724145851725]
  CropFormer is designed to tackle the intractability of instance-level segmentation on high-resolution images. It improves mask prediction by fusing high-res image crops that provide more fine-grained image details and the full image. With CropFormer, we achieve a significant AP gain of $1.9$ on the challenging entity segmentation task.
  arXiv  Detail & Related papers  (2022-11-10T18:58:22Z)
• BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations [89.42397034542189]
  We synthesize a large labeled dataset via a generative adversarial network (GAN) We take image samples from the class-conditional generative model BigGAN trained on ImageNet, and manually annotate 5 images per class, for all 1k classes. We create a new ImageNet benchmark by labeling an additional set of 8k real images and evaluate segmentation performance in a variety of settings.
  arXiv  Detail & Related papers  (2022-01-12T20:28:34Z)
• Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization [112.68171734288237]
  We propose a novel framework for discriminative pixel-level tasks using a generative model of both images and labels. We learn a generative adversarial network that captures the joint image-label distribution and is trained efficiently using a large set of unlabeled images. We demonstrate strong in-domain performance compared to several baselines, and are the first to showcase extreme out-of-domain generalization.
  arXiv  Detail & Related papers  (2021-04-12T21:41:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.