GeneSIS-RT: Generating Synthetic Images for training Secondary Real-world Tasks

by   Gregory J. Stein, et al.

We propose a novel approach for generating high-quality, synthetic data for domain-specific learning tasks, for which training data may not be readily available. We leverage recent progress in image-to-image translation to bridge the gap between simulated and real images, allowing us to generate realistic training data for real-world tasks using only unlabeled real-world images and a simulation. GeneSIS-RT ameliorates the burden of having to collect labeled real-world images and is a promising candidate for generating high-quality, domain-specific, synthetic data. To show the effectiveness of using GeneSIS-RT to create training data, we study two tasks: semantic segmentation and reactive obstacle avoidance. We demonstrate that learning algorithms trained using data generated by GeneSIS-RT make high-accuracy predictions and outperform systems trained on raw simulated data alone, and as well or better than those trained on real data. Finally, we use our data to train a quadcopter to fly 60 meters at speeds up to 3.4 m/s through a cluttered environment, demonstrating that our GeneSIS-RT images can be used to learn to perform mission-critical tasks.



There are no comments yet.


page 1

page 3

page 4

page 6

page 7


Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation

In the medical domain, the lack of large training data sets and benchmar...

A Simple Domain Shifting Networkfor Generating Low Quality Images

Deep Learning systems have proven to be extremely successful for image r...

Learn to Differ: Sim2Real Small Defection Segmentation Network

Recent studies on deep-learning-based small defection segmentation appro...

Novel tracking approach based on fully-unsupervised disentanglement of the geometrical factors of variation

Efficient tracking algorithm is a crucial part of particle tracking dete...

EXPO-HD: Exact Object Perception using High Distraction Synthetic Data

We present a new labeled visual dataset intended for use in object detec...

Synthetic Examples Improve Generalization for Rare Classes

The ability to detect and classify rare occurrences in images has import...

Keep it Unreal: Bridging the Realism Gap for 2.5D Recognition with Geometry Priors Only

With the increasing availability of large databases of 3D CAD models, de...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.