CONTROLLABLE PARETO MULTI-TASK LEARNING

Abstract

A multi-task learning (MTL) system aims at solving multiple related tasks at the same time. With a fixed model capacity, the tasks would be conflicted with each other, and the system usually has to make a trade-off among learning all of them together. Multiple models with different preferences over tasks have to be trained and stored for many real-world applications where the trade-off has to be made online. This work proposes a novel controllable Pareto multi-task learning framework, to enable the system to make real-time trade-off switch among different tasks with a single model. To be specific, we formulate the MTL as a preference-conditioned multiobjective optimization problem, for which there is a parametric mapping from the preferences to the Pareto stationary solutions. A single hypernetwork-based multi-task neural network is built to learn all tasks with different trade-off preferences among them, where the hypernetwork generates the model parameters conditioned on the preference. For inference, MTL practitioners can easily control the model performance based on different trade-off preferences in real-time. Experiments on different applications demonstrate that the proposed model is efficient for solving various multi-task learning problems.

1. INTRODUCTION

Figure 1 : Controllable Pareto MTL allows practitioners to control the trade-offs among tasks in real time with a single model, which could be desirable for many MTL applications. Multi-task learning (MTL) is important for many real-world applications, such as computer vision (Kokkinos, 2017), natural language processing (Subramanian et al., 2018) , and reinforcement learning (Van Moffaert & Nowé, 2014) . In these problems, multiple tasks are needed to be learned at the same time. An MTL system usually builds a single model to learn several related tasks together, in which the positive knowledge transfer could improve the performance for each task. In addition, using one model to conduct multiple tasks is also good for saving storage costs and reducing the inference time, which could be crucial for many applications (Standley et al., 2020) . However, with fixed learning capacity, different tasks could be conflicted with each other, and can not be optimized simultaneously (Zamir et al., 2018) . The practitioners might need to carefully design and train the tasks into different groups to achieve the best performance (Standley et al., 2020) . A considerable effort is also needed to find a set of suitable weights to balance the performance of each task (Kendall et al., 2018; Chen et al., 2018b; Sener & Koltun, 2018) . For some applications, it might need to train and store multiple models for different trade-off preferences among the tasks (Lin et al., 2019) . In many real-world MTL applications, the system will need to make a trade-off among different tasks in real time, and it is desirable to have the whole set of optimal trade-off solutions. For example, in a self-driving system, multiple tasks must be conducted simultaneously but also compete for a fixed resource (e.g., fixed total inference time threshold), and their preferences could change in real time for different scenarios (Karpathy, 2019). A recommendation system needs to balance multiple criteria among different stakeholders simultaneously, and making trade-off adjustment would be a crucial component (Milojkovic et al., 2019) . Consider the huge storage cost, it is far from ideal to

