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.
Pioneer Networks: Progressively Growing Generative Autoencoder
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abstract
We introduce a novel generative autoencoder network model that learns to encode and reconstruct images with high quality and resolution, and supports smooth random sampling from the latent space of the encoder. Generative adversarial networks (GANs) are known for their ability to simulate random high-quality images, but they cannot reconstruct existing images. Previous works have attempted to extend GANs to support such inference but, so far, have not delivered satisfactory high-quality results. Instead, we propose the Progressively Growing Generative Autoencoder (PIONEER) network which achieves high-quality reconstruction with $128{\times}128$ images without requiring a GAN discriminator. We merge recent techniques for progressively building up the parts of the network with the recently introduced adversarial encoder-generator network. The ability to reconstruct input images is crucial in many real-world applications, and allows for precise intelligent manipulation of existing images. We show promising results in image synthesis and inference, with state-of-the-art results in CelebA inference tasks.
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cs.CV 1years
2019 1verdicts
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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.