In this paper, we investigate the extrapolation capabilities of implicit deep learning models in handling unobserved data, where traditional deep neural networks may falter. Implicit models, distinguished by their adaptability in layer depth and incorporation of feedback within their computational graph, are put to the test across various extrapolation scenarios: out-of-distribution, geographical, and temporal shifts. Our experiments consistently demonstrate significant performance advantage with implicit models. Unlike their non-implicit counterparts, which often rely on meticulous architectural design for each task, implicit models demonstrate the ability to learn complex model structures without the need for task-specific design, highlighting their robustness in handling unseen data.

翻译：本文研究了隐式深度学习模型在处理传统深度神经网络可能失效的未观测数据时的泛化能力。隐式模型以其在层深度上的自适应性及计算图中融入反馈机制为特点，在多种泛化场景中接受了测试：包括分布外、地理空间及时序偏移。我们的实验一致表明，隐式模型具有显著的性能优势。与非隐式模型通常需要为每个任务精心设计网络架构不同，隐式模型展现了无需针对特定任务进行设计即可学习复杂模型结构的能力，这突显了其在处理未见数据时的鲁棒性。