RL-I2IT:图像对图像的翻译与深度强化学习.
Jing Hu1, Ziwei Luo2, Chengming Feng1
1School of Computer Science, Chengdu University of Information Technology, Chengdu, Sichuan, 610225, China.
概括
这项研究引入了一种基于强化学习的图像对图像翻译 (RL-I2IT) 框架. 它有效地将复杂的图像翻译分解为使用轻量级模型的代步骤,提高性能并减少过拟合.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 现有的图像对图像翻译 (I2IT) 方法经常使用单步深度学习 (DL) 模型,这些模型可以是参数繁重且容易过拟合的.
- 设计高效和强大的I2IT模型仍然是一个挑战,特别是在高维的连续状态和动作空间.
研究的目的:
- 将图像对图像翻译 (I2IT) 重构为使用深度强化学习 (DRL) 的代决策问题.
- 提出一个基于RL的计算效率高的I2IT (RL-I2IT) 框架,将单一的学习分解成更小,更容易管理的步骤.
- 为I2IT.在DRL中解决高维连续状态和动作空间的挑战.
主要方法:
- 拟议的RL-I2IT框架采用深度强化学习方法,灵感来自扩散模型.
- 它将I2IT过程分解为代步骤,使用轻量级模型进行渐进的图像转换.
- 在Actor-Critic模型中引入了一个具有低维"概念计划"的元策略,以有效地处理高维的行动空间.
- 使用特定任务的辅助学习策略来稳定培训并提高绩效.
主要成果:
- 对各种I2IT任务的实验证明了RL-I2IT框架的有效性和稳定性.
- 该方法在处理高维的连续动作空间问题方面具有显著的优势.
- 与单步方法相比,代分解方法带来了更高的计算效率和更少的过度解决方案.
结论:
- 通过利用深度增强学习,RL-I2IT框架为图像对图像翻译提供了一种新且高效的方法.
- 提出的方法有效地解决了传统的单步DL模型的局限性,特别是在管理复杂,高维的转换方面.
- 代的,逐步的转型策略为各种I2IT任务提供了强大而高性能的解决方案.
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