系统梳理多目标深度学习方法，涵盖主流算法与应用。
Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art
- 按训练算法与决策需求构建多目标深度学习分类体系
- 总结监督、无监督、强化学习及生成模型中的最新进展
- 适合研究多目标优化与深度学习交叉方向的读者
在机器学习中同时考虑多个目标已流行数十年，其优势包括多任务学习、稀疏性等次要目标的引入以及多准则超参数调优。然而，由于多目标优化相比单目标优化成本显著更高，近年来深度学习架构的兴起带来了额外挑战：参数量大、非线性强且存在随机性。本文综述了多目标深度学习领域的最新进展。首先提出一种基于训练算法类型和决策者需求的方法分类体系；随后列举近期技术突破与成功应用。涵盖监督学习、无监督学习、强化学习三大主流范式，并重点分析近年来备受关注的生成建模领域。
原文摘要 · Abstract (English)
Simultaneously considering multiple objectives in machine learning has been a popular approach for several decades, with various benefits for multi-task learning, the consideration of secondary goals such as sparsity, or multicriteria hyperparameter tuning. However - as multi-objective optimization is significantly more costly than single-objective optimization - the recent focus on deep learning architectures poses considerable additional challenges due to the very large number of parameters, strong nonlinearities and stochasticity. This survey covers recent advancements in the area of multi-objective deep learning. We introduce a taxonomy of existing methods - based on the type of training algorithm as well as the decision maker's needs - before listing recent advancements, and also successful applications. All three main learning paradigms supervised learning, unsupervised learning and reinforcement learning are covered, and we also address the recently very popular area of generative modeling.
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