Multi-Label Multi-Task Learning with Dynamic Task Weight Balancing
Multi-Label Multi-Task Learning with Dynamic Task Weight Balancing
复制标题
DOI：
10.1109/iri49571.2020.00042
复制
发表时间：
2020-08
期刊：
影响因子：
--
通讯作者：
Tianyi Wang;Shu‐Ching Chen
中科院分区：
文献类型：
--
作者：
Tianyi Wang;Shu‐Ching Chen
从真实世界环境中收集的数据通常包含多个对象、场景和活动。与每个数据样本只定义一个概念的单标签问题相比，多标签问题允许多个概念共存。为了充分利用真实世界数据中丰富的语义信息，多标签分类在各个领域得到了广泛的应用。多标签问题的传统方法往往会产生内存使用增加，模型推理速度慢，最重要的是概念之间的依赖关系利用不足的副作用。在本文中，我们采用多任务学习来应对这些挑战。多任务学习将每个概念的学习视为一项单独的工作，同时利用所有任务之间的共享表示。我们还提出了一个动态的任务平衡方法，自动调整任务的权重分配，同时考虑样本级和任务级的学习复杂性。我们的框架进行评估的灾难视频数据集和性能进行比较，与几个国家的最先进的多标签和多任务学习技术。结果证明了我们的方法的有效性和优越性。
Data collected from real-world environments often contain multiple objects, scenes, and activities. In comparison to single-label problems, where each data sample only defines one concept, multi-label problems allow the co-existence of multiple concepts. To exploit the rich semantic information in real-world data, multi-label classification has seen many applications in a variety of domains. The traditional approaches to multi-label problems tend to have the side effects of increased memory usage, slow model inference speed, and most importantly the under-utilization of the dependency across concepts. In this paper, we adopt multi-task learning to address these challenges. Multi-task learning treats the learning of each concept as a separate job, while at the same time leverages the shared representations among all tasks. We also propose a dynamic task balancing method to automatically adjust the task weight distribution by taking both sample-level and task-level learning complexities into consideration. Our framework is evaluated on a disaster video dataset and the performance is compared with several state-of-the-art multi-label and multi-task learning techniques. The results demonstrate the effectiveness and supremacy of our approach.