室内攀爬路径的变分自编码器嵌入与生成
Embedding and generation of indoor climbing routes with variational autoencoder
摘要：
最近室内攀岩的流行度增加允许深度学习算法的可能应用，以分类和生成攀岩路线。在这项工作中，我们采用了一个变分自编码器，攀登路线在一个标准化的训练设备MoonBoard，一个著名的训练工具在攀登社区。通过对编码的潜在空间进行采样，可以观察到该算法可以生成高质量的攀爬路线。生成的22个问题被上传到Moonboard应用程序供用户查看。该算法可以作为第一步，以方便室内攀登路线设置。
英文摘要：
Recent increase in popularity of indoor climbing allows possible applications of deep learning algorthms to classify and generate climbing routes. In this work, we employ a variational autoencoder to climbing routes in a standardized training apparatus MoonBoard, a well-known training tool within the climbing community. By sampling the encoded latent space, it is observed that the algorithm can generate high quality climbing routes. 22 generated problems are uploaded to the Moonboard app for user review. This algorithm could serve as a first step to facilitate indoor climbing route setting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
