Today, we begin a new mini-series that marks a slight change in the direction of the series. Previously, we have talked about the history of synthetic data (one, two, three, four) and reviewed a recent paper on synthetic data. This time, we begin a series devoted to a specific machine learning problem that is often supplemented by the use of synthetic data: object detection. In this first post of the series, we will discuss what the problem is and where the data for object detection comes from and how you can get your network to detect bounding boxes like below (image source).
We have been talking about the history of synthetic data for quite some time, but it’s time to get back to 2020! I’m preparing a new series, but in the meantime, today we discuss a paper called “Learning From Context-Agnostic Synthetic Data” by MIT researchers Charles Jin and Martin Rinard, recently released on arXiv (it’s less than a month old). They present a new way to train on synthetic data based on few-shot learning, claiming to need very few synthetic examples; in essence, their paper extends the cut-n-paste approach to generating synthetic datasets. Let’s find out more and, pardon the pun, give their results some context.
Last time, we talked about robotic simulations in general: what they are and why they are inevitable for robotics based on machine learning. We even touched upon some of the more philosophical implications of simulations in robotics, discussing early concerns on whether simulations are indeed useful or may become a dead end for the field. Today, we will see the next steps of robotic simulations, showing how they progressed after the last post with the example of MOBOT, a project developed in the first half of the 1990s in the University of Kaiserslautern. This is another relatively long read and the last post in the “History of Synthetic Data” series.
In the previous two blog posts, we have discussed the origins and first applications of synthetic data. The first part showed how early computer vision used simple line drawings for scene understanding algorithms and how synthetic datasets were necessary as test sets to compare different computer vision algorithms. In the second part, we saw how self-driving cars were made in the 1980s and how the very first application of machine learning in computer vision for autonomous vehicles, the ALVINN system, was trained on synthetic data. Today, we begin the discussion of early robotics and the corresponding synthetic simulators… but this first part will be a bit more philosophical than usual.