Episodic task agnostic contrastive training for multi-task learning

Abstract

Learning knowledge from different tasks to improve the general learning performance is crucial for designing an efficient algorithm. In this work, we tackle the Multi-task Learning (MTL) problem, where the learner extracts the knowledge from different tasks simultaneously with limited data. Previous works have been designing the MTL models by taking advantage of the transfer learning techniques, requiring the knowledge of the task index, which is not realistic in many practical scenarios. In contrast, we consider the scenario that the task index is not explicitly known, under which the features extracted by the neural networks are task agnostic. To learn the task agnostic invariant features, we implement model agnostic meta-learning by leveraging the episodic training scheme to capture the common features across tasks. Apart from the episodic training scheme, we further implemented a contrastive learning objective to improve the feature compactness for a better prediction boundary in the embedding space. We conduct extensive experiments on several benchmarks compared with several recent strong baselines to demonstrate the effectiveness of the proposed method. The results showed that our method provides a practical solution for real-world scenarios, where the task index is agnostic to the learner and can outperform several strong baselines, achieving state-of-the-art performances.