Data-Efficient Deep Learning
During the past few years, deep neural networks have become the de-facto technique for machine learning and computer vision tasks, in many cases achieving human- or super-human-level performance by leveraging large collections of training data. However, this success comes at a distinct cost; namely, creating these large datasets typically requires a great deal of human effort (collecting and manually labelling data samples), pain or risk (e.g. for medical datasets involving invasive tests) and financial expense (building the infrastructure needed for domain-specific data collection and hiring labelers). For many real-world applications, lack of training data often becomes a restrictive factor, which limits the utilization of deep learning techniques in practice. In our research we focus on relaxing this constraint and exploiting solutions for data-efficient deep learning.
One of the promising directions is to employ synthetic data. Synthetic data generation with generative models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), is a relatively recent development with huge potential for data-efficient deep learning. At Bosch IoC Lab we aim to investigate unsupervised and semi-supervised methods to extract patterns from available data that allow synthesizing data points that are almost indistinguishable from real data. This can help to vastly reduce the need to collect new data, and improve the performance of deep learning methods trained on both real and synthetic data. Furthermore, we aim to investigate domain transfer methods, which transform real data to a new setting, e.g. translate images between different camera sensor models, change light and weather conditions of collected images and videos, or adapt image and video data to new locations. Data-efficient methods for deep learning have huge cost saving potential, and can also contribute to making algorithms safer and more robust to deal with safety-critical situations, e.g. by synthesizing rare data points or dangerous situations for which almost no real-world data is available.
Dr. Anna Khoreva
Research Group Leader at Bosch Center for Artificial Intelligence (BCAI)
research focus: data-efficient deep learning, with a particular focus on generative models, image and video synthesis, few-shot learning, unsupervised and weakly supervised learning