Scalable Pareto Front Approximation for Deep Multi-Objective Learning

Multi-objective optimization is important for various Deep Learning applications, however, no prior multi-objective method suits very deep networks. Existing approaches either require training a new network for every solution on the Pareto front or add a considerable overhead to the number of parameters by introducing hyper-networks conditioned on modifiable preferences. In this paper, we present a novel method that contextualizes the network directly on the preferences by adding them to the input space. In addition, we ensure a well-spread Pareto front by forcing the solutions to preserve a small angle to the preference vector. Through extensive experiments, we demonstrate that our Pareto fronts achieve state-of-the-art quality despite being computed significantly faster. Furthermore, we demonstrate the scalability as our method approximates the full Pareto front on the CelebA dataset with an EfficientNet network at a marginal training time overhead of 7% compared to a single-objective optimization.