相关实验视频
Updated: Jul 12, 2025

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Using Generative Art to Convey Past and Future Climate Transitions
Published on: March 31, 2023
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可控制的无监督的雪合成通过潜式风格的空间操纵
Hanting Yang1, Alexander Carballo2,3,4, Yuxiao Zhang1
1Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
本研究介绍了智能汽车技术的新型生成对抗网络 (GAN). 该模型合成了具有挑战性的驾驶图像,改善了感知算法,而不需要真实世界的数据.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 智能汽车技术 智能汽车技术
背景情况:
- 智能车辆感知算法需要多样化的训练数据,通常是难以获得和危险的.
- 目前的未配对的图像对图像翻译方法缺乏对生成和多个输出的控制.
研究的目的:
- 开发可控制的图像对图像转换模型,用于合成具有挑战性的驾驶场景.
- 为了提高智能汽车感知算法的稳定性.
主要方法:
- 提出了一个具有风格和内容编码器的生成对抗网络 (GAN).
- 使用解码器从编码的特征进行图像重建.
- 实现了一个自我回归模块,以限制可控生成的风格隐藏空间.
主要成果:
- 在城市景观和欧洲城市人物数据集上成功生成了雪景.
- 通过修改超参数和样式代码来证明可控制的输出生成.
- 验证了模型在合成现实和多样化的图像域中的有效性.
结论:
- 拟议的GAN模型有效地合成了智能汽车的多样化和具有挑战性的视觉数据.
- 这种方法减少了对现实世界数据采集的依赖,加速了算法开发.
- 可控制的生成能力为强大的感知系统训练提供了显著的优势.
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