Addressing the challenge of data scarcity in industrial domains, transfer
learning emerges as a pivotal paradigm. This work introduces Style Filter, a
tailored methodology for industrial contexts. By selectively filtering source
domain data before knowledge transfer, Style Filter reduces the quantity of
data while maintaining or even enhancing the performance of transfer learning
strategy. Offering label-free operation, minimal reliance on prior knowledge,
independence from specific models, and re-utilization, Style Filter is
evaluated on authentic industrial datasets, highlighting its effectiveness when
employed before conventional transfer strategies in the deep learning domain.
The results underscore the effectiveness of Style Filter in real-world
industrial applications.

针对工业领域中数据稀缺的挑战，转移学习成为一种关键的范式。该研究介绍了一种专门针对工业环境的方法学，称为 Style Filter。通过在知识转移之前有选择性地过滤源领域数据，Style Filter 减少了数据的数量，同时保持或增强了转移学习策略的性能。Style Filter 在深度学习领域的传统转移策略之前使用，并在真实工业数据集上进行了评估，突出了其在实际工业应用中的效果。

利用样式过滤增强工业迁移学习：成本减少与缺陷焦点

Enhancing Industrial Transfer Learning with Style Filter: Cost Reduction  and Defect-Focus

The CDCL algorithm is the leading solution adopted by state-of-the-art
solvers for SAT, SMT, ASP, and others. Experiments show that the performance of
CDCL solvers can be significantly boosted by embedding domain-specific
heuristics, especially on large real-world problems. However, a proper
integration of such criteria in off-the-shelf CDCL implementations is not
obvious. In this paper, we distill the key ingredients that drive the search of
CDCL solvers, and propose a general framework for designing and implementing
new heuristics. We implemented our strategy in an ASP solver, and we
experimented on two industrial domains. On hard problem instances,
state-of-the-art implementations fail to find any solution in acceptable time,
whereas our implementation is very successful and finds all solutions.

通过嵌入特定领域的启发式方法来提高 CDCL 算法的性能并不容易，本文提出了一种通用框架来设计和实现新的启发式方法，并在两个工业领域实验中取得成功。