多任务学习效率的鲁棒性分析：轻量级骨干网络的新基准与基于特征解耦的多任务学习挑战有效度量
Robust Analysis of Multi-Task Learning Efficiency: New Benchmarks on Light-Weighed Backbones and Effective Measurement of Multi-Task Learning Challenges by Feature Disentanglement
AI总结：
针对现有多任务学习方法在轻量骨干网络表现、快速梯度近似技术泛化性及挑战识别方法不足的问题，本文开展相关基准实验并提出基于特征解耦的挑战度量方法及对应评估指标。
摘要：
多任务学习（MTL）的核心动机之一是开发能够同时推理多个任务的神经网络。过去十年间，学界提出了无数研究鲁棒模型架构与高效训练算法的方法，但这些方法应用于更小的特征提取骨干网络时的表现、常用的“以特征级梯度替代参数级梯度”的快速近似技术的泛化性，以及对MTL挑战的全面认知与高效识别方法仍存在空白。本文聚焦现有MTL方法的上述效率相关问题。首先，我们在更小的骨干网络上开展大规模实验，并将MetaGraspNet数据集作为新的测试基准。其次，我们对比了使用与不使用快速梯度替代方法的现有MTL方法，通过实证研究该技术的泛化性。最后，我们提出特征解耦度量（Feature Disentanglement measure）作为一种新颖高效的MTL挑战识别方法，并提出排序相似度得分（Ranking Similarity score）作为不同识别方法的评估指标，以验证本文方法的可靠性。
英文摘要：
One of the main motivations of MTL is to develop neural networks capable of inferring multiple tasks simultaneously. While countless methods have been proposed in the past decade investigating robust model architectures and efficient training algorithms, there is still lack of understanding of these methods when applied on smaller feature extraction backbones, the generalizability of the commonly used fast approximation technique of replacing parameter-level gradients with feature level gradients, and lack of comprehensive understanding of MTL challenges and how one can efficiently and effectively identify the challenges. In this paper, we focus on the aforementioned efficiency aspects of existing MTL methods. We first carry out large-scale experiments of the methods with smaller backbones and on a the MetaGraspNet dataset as a new test ground. We also compare the existing methods with and without using the fast gradient surrogate and empirically study the generalizability of this technique. Lastly, we propose Feature Disentanglement measure as a novel and efficient identifier of the challenges in MTL, and propose Ranking Similarity score as an evaluation metric for different identifiers to prove the faithfulness of our method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
