Today, I continue the series about different ways of improving model performance with synthetic data. We have already discussed simple augmentations in the first post and “smart” augmentations that make more complex transformations of the input in the second. Today we go on to the next sub-topic: domain adaptation. We will stay with domain adaptation for a while, and in the first post on this topic I would like to present a general overview of the field and introduce the most basic approaches to domain adaptation.
Last time, I started a new series of posts, devoted to different ways of improving model performance with synthetic data. In the first post of the series, we discussed probably the simplest and most widely used way to generate synthetic data: geometric and color data augmentation applied to real training data. Today, we take the idea of data augmentation much further. We will discuss several different ways to construct “smart augmentations” that make much more involved transformations of the input but still change the labeling only in predictable ways.
Welcome back, everybody! It’s been a while since I finished the last series on object detection with synthetic data (here is the series in case you missed it: part 1, part 2, part 3, part 4, part 5). So it is high time to start a new series. Over the next several posts, we will discuss how synthetic data and similar techniques can drive model performance and improve the results. We will mostly be talking about computer vision tasks. We begin this series with an explanation of data augmentation in computer vision; today we will talk about simple “classical” augmentations, and next time we will turn to some of the more interesting stuff.
This is the last post in my mini-series on object detection with synthetic data. Over the first four posts, we introduced the problem, discussed some classical synthetic datasets for object detection, talked about some early works that have still relevant conclusions and continued with a case study on retail and food object detection. Today we consider two papers from 2019 that still represent the state of the art in object detection with synthetic data and are often used as generic references to the main tradeoffs inherent in using synthetic data. We will see and discuss those tradeoffs too. Is synthetic data ready for production and how does it compare with real in object detection? Let’s find out. (header image source)
We continue the series on synthetic data for object detection. Last time, we stopped in 2016, with some early works on synthetic data for deep learning that still have implications relevant today. This time, we look at a couple of more recent papers devoted to multiple object detection for food and small vendor items. As we will see today, such objects are a natural application for synthetic data, and we’ll see how this application has evolved in the last few years.
Today, I continue the series on synthetic data for object detection. In the first post of the series, we discussed the object detection problem itself and real world datasets for it, and the second was devoted to popular synthetic datasets of common objects. The time has come to put this data in practice: in this and subsequent posts, we will discuss common contemporary object detection architectures and see how adding synthetic data fares for object detection as reported in literature. In each post, I will give a detailed account of one paper that stands out in my opinion and briefly review one or two more. We begin in 2015.
In the last post, we started talking about object detection. We discussed what the problem is, saw the three main general-purpose real-world datasets for object detection, and began talking about synthetic data. Today, we continue the series with a brief overview of the most important synthetic datasets for object detection. Last time, I made an example of an autonomous driving dataset, but this is a topic of its own, and so are, say, synthetic images of people and human faces. Today, we will concentrate on general-purpose and household object datasets.
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.