Deep Learning for Recognition of Objects, Activities, Faces, and Spatio-Temporal Patterns

The origin of deep learning is in computer vision. However, researchers found that deep learning is a very powerful tool to solve many problems in other areas like forecasting, finance, human pose estimation, NLP, etc. Deep learning based methods showed a wonderful performance relate to other available methods. We have tried to improve deep learning methods and using them for solving problems in different areas. In this thesis, we will try to use the deep learning techniques for solving problems in different areas such as unsupervised learning, object classification, forecasting, cognitive behavior assessment and face recognition. In the computer vision part, a novel method for unsupervised feature learning for image classification was proposed in the thesis. Training a CNN needs huge amount of data. So, finding the methods to train CNN with unlabeled data is very promising. In the second part, we proposed a new deep learning based framework for forecasting. Forecasting is a challenging task and has many applications in finance, meteorology, etc. We have proposed a new framework for forecasting in cases that there are many nodes to generate data. One application of our framework is prediction of the wind speed for multiple stations around the country. Another problem that we have been using DL to solve is face recognition at scale. Face recognition is very demanding both in academic and industry. Also, we used DL to improve the performance of the system for behavioral assessment.