Basic Visual Machine Learning
- 1 minVisual Learning
Abstract
Visual learning has enormous potential to solve previously impossible problems in machine perception. The recent deep learning breakthrough from the machine learning community has allowed researchers not only to address new visual learning problems, but also to solve old problems. In general, the success of deep learning is attributed to the vast computational resources available and large annotated datasets containing millions of images. In spite of the excitement generated by these recent developments, there is a lack of understanding of how deep learning works, which invites questions about convergence, stability and robustness of such models. This program addresses important challenges in deep learning, such as: effective transfer learning, role of probabilistic graphical models in deep learning, efficient training and inference algorithms, etc. Answering these questions will allow us to design and implement robust visual learning systems that will help our robots fully understand the environment around them.
I have been working in the Basic Visual Machine Learning within the Australian Centre of Robotic Vision (ACRV). If you wish to know more about our work, please check the links below.
Rafa Felix
PhD, that climbs and enjoy long distance rides.