Learning with Constraint Learning: New Perspective, Solution Strategy and Various Applications

• URL: http://arxiv.org/abs/2307.15257v1
• Date: Fri, 28 Jul 2023 01:50:27 GMT
• Title: Learning with Constraint Learning: New Perspective, Solution Strategy and Various Applications
• Authors: Risheng Liu, Jiaxin Gao, Xuan Liu, and Xin Fan
• Abstract summary: We propose a new framework, named Learning with Constraint Learning (LwCL), that can holistically examine challenges. LwCL is designed as a general hierarchical optimization model that captures the essence of diverse learning and vision problems. Our proposed framework efficiently addresses a wide range of applications in learning and vision, encompassing three categories and nine different problem types.
• Score: 45.45917703420217
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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