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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