6 citations · 12 across the 4 of their papers we have counts for
4 papers
DreamTeacher: Pretraining Image Backbones with Deep Generative Models
Daiqing Li, Huan Ling, Amlan Kar +5
In this work, we introduce a self-supervised feature representation learning framework DreamTeacher that utilizes generative networks for pre-training downstream image backbones. W…
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks
Rafid Mahmood, James Lucas, David Acuna +6
Given a small training data set and a learning algorithm, how much more data is necessary to reach a target validation or test performance? This question is of critical importance…
BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations
Daiqing Li, Huan Ling, Seung Wook Kim +4
Annotating images with pixel-wise labels is a time-consuming and costly process. Recently, DatasetGAN showcased a promising alternative - to synthesize a large labeled dataset via…
Neural Turtle Graphics for Modeling City Road Layouts
Hang Chu, Daiqing Li, David Acuna +6
We propose Neural Turtle Graphics (NTG), a novel generative model for spatial graphs, and demonstrate its applications in modeling city road layouts. Specifically, we represent the…