University project

Implementation of a deep learning detection model to detect pedestrians in images taken at night from the NightOwls dataset. The model was implemented with Python and the Keras API for TensorFlow and trained on the Google Cloud Platform.

Implementation of a Generative Adversarial Network (GAN) that generates faces with specified features. The network was trained on the CelebA dataset and was implemented using Pytorch. It was the project for the course of deep learning at the University of Trento.

Project for the advanced deep learning course at KTH where the task was to choose a paper from NIPS 2019 and reproduce it. The chosen paper was about transfer learning: the objective was to train a network from another already trained network (the “teacher”) without any of the data the teacher network was trained with.

Plan a path for a wheeled robot: given an image taken from the top of a robot in an environment with obstacles and a goal, the objective of the project was to plan a path to reach the goal, avoiding the obstacles.

Project to modify the code of the paper “Social LSTM: Human Trajectory Prediction in Crowded Spaces” to improve accuracy, which was written in Python with Tensorflow. The core idea of the paper was to predict the trajectories of pedestrians using LSTM neural networks.