This repository provides the PyTorch implementation of the MGDA_UB algorithm from the NeurIPS 2018 paper “Multi-Task Learning as Multi-Objective Optimization” by Sener and Koltun. The code implements both Frank-Wolfe and projected gradient descent optimization methods for training neural networks with multiple objectives. A generic numpy-only version is also provided for portability to other deep learning frameworks.