It is hyperbole to say deep learning is achieving state-of-the-art results across a range of difficult problem domains. There is a lot of excitement around artificial intelligence, machine learning and deep learning at the moment. It is also an amazing opportunity to get on on the ground floor of some really powerful tech. I try hard to convince friends, colleagues and students to get started in deep learning and bold statements like the above are not enough. It requires stories, pictures and research papers.
Instant Visual Translation Example of instant visual translation, taken from the Google Blog. A more complex variation of this task called object detection involves specifically identifying one or more objects within the scene of the photograph and drawing a box around them. In 2014, there were an explosion of deep learning algorithms achieving very impressive results on this problem, leveraging the work from top models for object classification and object detection in photographs. Generally, the systems involve the use of very large convolutional neural networks for the object detection in the photographs and then a recurrent neural network like an LSTM to turn the labels into a coherent sentence.
You might not know it, but deep learning already plays a part in our everyday life. When you speak to your phone via Cortana, Siri or Google Now and it fetches information, or you type in the Google search box and it predicts what you are looking for before you finish, you are doing something that has only been made possible by deep learning. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. It also is known as deep structured learning or hierarchical learning. The term Deep Learning was introduced to the machine learning community by Rina Dechter in 1986, and to Artificial Neural Networks by Igor Aizenberg and colleagues in 2000, in the context of Boolean threshold neurons.
The field of computer vision is shifting from statistical methods to deep learning neural network methods. There are still many challenging problems to solve in computer vision. Nevertheless, deep learning methods are achieving state-of-the-art results on some specific problems. It is not just the performance of deep learning models on benchmark problems that is most interesting; it is the fact that a single model can learn meaning from images and perform vision tasks, obviating the need for a pipeline of specialized and hand-crafted methods. In this post, you will discover nine interesting computer vision tasks where deep learning methods are achieving some headway.