论文标题
利用甘斯改善连续路径键盘输入模型
Leveraging GANs to Improve Continuous Path Keyboard Input Models
论文作者
论文摘要
连续路径键盘输入比标准敲击具有更高的固有歧义，因为路径跟踪不仅可能表现出局部过冲/不足的速度（如在敲击中），而且还取决于用户，大量的中路段派遣。因此，部署坚固的解决方案需要大量高质量的培训数据，这很难收集/注释。在这项工作中，我们通过使用gans使用用户现实的合成数据来扩大我们的培训语料库来应对这一挑战。实验表明，即使GAN生成的数据并未捕获真实用户数据的所有特征，但它仍然可以在5：1 GAN与现实比率下实质上提高准确性。因此，通过大大增加单词覆盖范围和路径多样性，甘斯在模型中注入了更多的鲁棒性。
Continuous path keyboard input has higher inherent ambiguity than standard tapping, because the path trace may exhibit not only local overshoots/undershoots (as in tapping) but also, depending on the user, substantial mid-path excursions. Deploying a robust solution thus requires a large amount of high-quality training data, which is difficult to collect/annotate. In this work, we address this challenge by using GANs to augment our training corpus with user-realistic synthetic data. Experiments show that, even though GAN-generated data does not capture all the characteristics of real user data, it still provides a substantial boost in accuracy at a 5:1 GAN-to-real ratio. GANs therefore inject more robustness in the model through greatly increased word coverage and path diversity.
