The complexity of learning problems, such as Generative Adversarial Network (GAN) and its variants, multi-task and meta-learning, hyper-parameter learning, and a variety of real-world vision applications, demands a deeper understanding of their underlying coupling mechanisms. Existing approaches often address these problems in isolation, lacking a unified perspective that can reveal commonalities and enable effective solutions. Therefore, in this work, we proposed a new framework, named Learning with Constraint Learning (LwCL), that can holistically examine challenges and provide a unified methodology to tackle all the above-mentioned complex learning and vision problems. Specifically, LwCL is designed as a general hierarchical optimization model that captures the essence of these diverse learning and vision problems. Furthermore, we develop a gradient-response based fast solution strategy to overcome optimization challenges of the LwCL framework. Our proposed framework efficiently addresses a wide range of applications in learning and vision, encompassing three categories and nine different problem types. Extensive experiments on synthetic tasks and real-world applications verify the effectiveness of our approach. The LwCL framework offers a comprehensive solution for tackling complex machine learning and computer vision problems, bridging the gap between theory and practice.

翻译：学习问题的复杂性，例如生成对抗网络（GAN）及其变体、多任务学习与元学习、超参数学习，以及多种现实世界的视觉应用，要求对其底层耦合机制有更深入的理解。现有方法常孤立地处理这些问题，缺乏能够揭示共性并提供有效解决方案的统一视角。因此，在本工作中，我们提出了一种新框架，名为带有约束学习的学习（LwCL），它能够全面地审视这些挑战，并提供统一的方法论来应对上述所有复杂的学习与视觉问题。具体而言，LwCL被设计为一种通用的分层优化模型，能够捕捉这些多样化学习与视觉问题的本质。此外，我们开发了一种基于梯度响应的快速求解策略，以克服LwCL框架中的优化难题。我们提出的框架高效地解决了学习与视觉领域中涵盖三个类别及九种不同类型问题的广泛应用。在合成任务与现实世界应用上的大量实验验证了本方法的有效性。LwCL框架为处理复杂机器学习与计算机视觉问题提供了综合性解决方案，弥合了理论与实际应用之间的差距。