The traditional Machine Learning (ML) methodology requires to fragment the development and experimental process into disconnected iterations whose feedback is used to guide design or tuning choices. This methodology has multiple efficiency and scalability disadvantages, such as leading to spend significant resources into the creation of multiple trial models that do not contribute to the final solution.The presented work is based on the intuition that defining ML models as modular and extensible artefacts allows to introduce a novel ML development methodology enabling the integration of multiple design and evaluation iterations into the continuous enrichment of a single unbounded intelligent system. We define a novel method for the generation of dynamic multitask ML models as a sequence of extensions and generalizations. We first analyze the capabilities of the proposed method by using the standard ML empirical evaluation methodology. Finally, we propose a novel continuous development methodology that allows to dynamically extend a pre-existing multitask large-scale ML system while analyzing the properties of the proposed method extensions. This results in the generation of an ML model capable of jointly solving 124 image classification tasks achieving state of the art quality with improved size and compute cost.
翻译:传统的机器学习(ML)方法要求将开发和实验过程分解为断开的迭代,其反馈用于指导设计或调整选择。这种方法具有多重效率和可缩放性缺点,例如导致大量资源用于创建多种试验模型,但无助于最终解决办法。 所提出的工作基于直觉,将ML模型定义为模块和可扩展的人工制品,从而可以引入一种新的ML发展方法,将多式设计和评价迭代纳入单一的无限制智能系统的连续浓缩中。我们界定了生成动态多任务ML模型的新方法,作为扩展和概括的顺序。我们首先通过使用标准的ML经验评估方法分析拟议方法的能力。最后,我们提出一个新的持续开发方法,以便能够动态扩展一个以前存在的多任务大型人工工艺系统,同时分析拟议方法扩展的特性。这导致生成一个ML模型,能够联合解决124个图像分类任务,从而以改良的大小和计算成本实现艺术质量状态。