提升多种抽象视觉推理任务下的泛化能力
Advancing Generalization Across a Variety of Abstract Visual Reasoning Tasks
AI总结：
针对抽象视觉推理任务中分布外泛化难的问题，提出具备组卷积、归一化和并行设计的PoNG模型，在多个基准上展现出更强的泛化能力。
摘要：
抽象视觉推理（AVR）领域包含一系列基于类比的多样化任务，用于研究模型的泛化能力。近年来该领域进展迅速，尤其是在独立同分布（i.i.d.）场景下，即模型在相同数据分布上训练和评估。然而，评估模型对新测试分布泛化能力的分布外（o.o.d.）设置，即使对最新模型而言仍具挑战性。为提升AVR任务中的泛化能力，我们提出归一化组卷积通路模型（PoNG），这是一种新型神经架构，具备组卷积、归一化和并行设计的特点。我们测试了广泛的AVR基准，包括Raven's Progressive Matrices以及包含合成图像和真实图像的视觉类比问题。实验表明，所提模型具备强大的泛化能力，在多个设置下性能优于现有文献中的方法。
英文摘要：
The abstract visual reasoning (AVR) domain presents a diverse suite of analogy-based tasks devoted to studying model generalization. Recent years have brought dynamic progress in the field, particularly in i.i.d. scenarios, in which models are trained and evaluated on the same data distributions. Nevertheless, o.o.d. setups that assess model generalization to new test distributions remain challenging even for the most recent models. To advance generalization in AVR tasks, we present the Pathways of Normalized Group Convolution model (PoNG), a novel neural architecture that features group convolution, normalization, and a parallel design. We consider a wide set of AVR benchmarks, including Raven's Progressive Matrices and visual analogy problems with both synthetic and real-world images. The experiments demonstrate strong generalization capabilities of the proposed model, which in several settings outperforms the existing literature methods.
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。
