Scalable Pareto Front Approximation for Deep Multi-Objective Learning

@article{Ruchte2021ScalablePF,
  title={Scalable Pareto Front Approximation for Deep Multi-Objective Learning},
  author={Michael Ruchte and Josif Grabocka},
  journal={2021 IEEE International Conference on Data Mining (ICDM)},
  year={2021},
  pages={1306-1311}
}
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… 
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