Efficiently Identifying Task Groupings for Multi-Task Learning (2109.04617v2)

Abstract: Multi-task learning can leverage information learned by one task to benefit the training of other tasks. Despite this capacity, naively training all tasks together in one model often degrades performance, and exhaustively searching through combinations of task groupings can be prohibitively expensive. As a result, efficiently identifying the tasks that would benefit from training together remains a challenging design question without a clear solution. In this paper, we suggest an approach to select which tasks should train together in multi-task learning models. Our method determines task groupings in a single run by training all tasks together and quantifying the effect to which one task's gradient would affect another task's loss. On the large-scale Taskonomy computer vision dataset, we find this method can decrease test loss by 10.0% compared to simply training all tasks together while operating 11.6 times faster than a state-of-the-art task grouping method.

• The paper introduces Task Affinity Grouping (TAG), which quantifies inter-task gradient influence to efficiently group tasks for joint training.
• It provides robust theoretical analysis in convex settings, validating improved loss reductions on benchmarks like Taskonomy and CelebA.
• TAG significantly reduces computation by avoiding exhaustive search, achieving up to 10% lower test loss and over tenfold speed gains.

Efficiently Identifying Task Groupings for Multi-Task Learning

The paper "Efficiently Identifying Task Groupings for Multi-Task Learning" presents a novel approach to address a persistent challenge in multi-task learning (MTL): efficiently determining which tasks should be grouped together for joint training. MTL aims to leverage the information learned from multiple tasks to improve performance across all tasks within a shared model architecture. However, indiscriminately training all tasks together can lead to suboptimal results due to conflicts, wherein tasks might compete for model capacity or fail to develop representations that generalize well across different objectives.

Problem Statement and Importance

The researchers focus on a significant problem within MTL—the challenge of determining which combinations of tasks should be trained together to maximize performance and optimize resource use. Exhaustive search for task groupings is computationally prohibitive, especially with an increasing number of tasks. This paper proposes a method that foregoes the need for exhaustive evaluation by efficiently identifying task groupings in one run. This solution is especially relevant given practical constraints on inference-time resource use, where models must maintain low latency and meet memory constraints while serving multiple task predictions.

Proposed Method: Task Affinity Grouping

The core contribution of the work is a method termed Task Affinity Grouping (TAG). TAG quantifies inter-task affinity by analyzing the extent to which the gradient update of one task influences the training loss of another. Specifically, during the training of a single multi-task model, it measures how one task's gradient impacts another task's loss, averaging these interactions across training to determine affinity scores. These scores facilitate systematic and efficient task group selection, wherein tasks demonstrating high mutual affinity are grouped together.

Theoretical Foundations

A highlight of the paper is its robust theoretical analysis, which supports the method’s validity in a convex optimization context. The authors prove that, under certain conditions, maximizing inter-task affinity indeed results in improved loss reductions—a crucial insight that validates TAG's approach to optimizing task groupings.

Empirical Evaluation

The empirical evaluation on datasets like CelebA and Taskonomy underscores TAG's efficacy. Notably, in the Taskonomy benchmarks, TAG reduces the computational cost of finding optimal task groupings by over an order of magnitude compared to existing methods while maintaining competitive performance. In practice, applied TAG achieves notable reductions in test loss—up to 10% improvements—compared to naively training all tasks together. Moreover, it operates significantly faster—over tenfold—compared to alternative high-order approximation methods.

Comparison with Alternative Approaches

The paper compares its methodology against traditional approaches like exhaustive search and heuristic-based strategies, achieving comparable or improved performance with markedly reduced computation time. It also competes favorably with advanced optimization techniques such as Uncertainty Weights, GradNorm, and PCGrad, indicating that optimizing task groupings surpasses merely adjusting optimization procedures for all-task models. Importantly, TAG’s effectiveness is attributed to its capacity to dynamically and precisely capture inter-task relationships as they evolve during training.

Implications and Future Directions

The implications of this research are both practical and theoretical. Practically, it provides a scalable and efficient tool for practitioners aiming to implement MTL in scenarios constrained by computational resources. Theoretically, it highlights the significance of dynamic task interactions in determining optimal training strategies. Future research may explore extending TAG to different MTL contexts or integrating model architecture optimizations to further enhance performance. Moreover, analyzing how TAG adapts to non-convex settings or its response to transfer learning paradigms offers promising avenues for further exploration.

In conclusion, this paper provides a meaningful step forward in the efficient training of multi-task models, balancing computational efficiency with task-specific performance gains. The authors present a well-founded, empirically validated approach that sets the stage for future advancements in adaptive and resource-efficient MTL methodologies.