Sharif Digital Repository

Large data sources labeled by experts at cost are essential for deep learning success in various domains. But, when labeling is expensive and labeled data is scarce, deep learning generally does not perform well. The goal of semi-supervised learning is to leverage abundant unlabeled data that one can easily collect. New semi-supervised algorithms based on data augmentation techniques have reached new advances in this field. In this work, by studying different textual augmentation techniques, a new approach is proposed that can obtain effective information signals from unlabeled data. The method encourages the model to generate the same representation vectors for different augmented versions... 

Nowadays, one of the challenges in social robotics is to navigate the robot in social environments with moving elements such as humans. The purpose of this study is to navigate the Arash 2 social robot in a dynamic environment autonomously without encountering moving obstacles (humans). The Arash 2 robot was first simulated in the Gazebo simulator environment in this research. The simultaneous location and mapping (SLAM) technique was implemented on the robot using a lidar sensor to obtain an environment map. Then, using the deep reinforcement learning approach, the neural network developed in the simulation environment was trained and implemented on the robot in the real environment. The...