Chaining a progressive GAN (for segmentation labels) and a conditional GAN (for imagery), the paper shows vehicle detection mAP improves by up to roughly 10% relative when augmenting very small Potsdam training sets with synthetic data, with gains disappearing on larger sets.
SpaceNet competition
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery
Chaining a progressive GAN (for segmentation labels) and a conditional GAN (for imagery), the paper shows vehicle detection mAP improves by up to roughly 10% relative when augmenting very small Potsdam training sets with synthetic data, with gains disappearing on larger sets.