Learning with Constraint Learning: New Perspective, Solution Strategy
  and Various Applications
Risheng Liu Jiaxin Gao Xuan Liu Xin Fan
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.