Recent work done on lane detection has been able to detect lanes accurately
in complex scenarios, yet many fail to deliver real-time performance
specifically with limited computational resources. In this work, we propose
SwiftLane: a simple and light-weight, end-to-end deep learning based framework,
coupled with the row-wise classification formulation for fast and efficient
lane detection. This framework is supplemented with a false positive
suppression algorithm and a curve fitting technique to further increase the
accuracy. Our method achieves an inference speed of 411 frames per second,
surpassing state-of-the-art in terms of speed while achieving comparable
results in terms of accuracy on the popular CULane benchmark dataset. In
addition, our proposed framework together with TensorRT optimization
facilitates real-time lane detection on a Nvidia Jetson AGX Xavier as an
embedded system while achieving a high inference speed of 56 frames per second.

本文提出了 SwiftLane 算法，这是一个采用端到端深度学习框架、基于行分类的轻量级车道检测方法，同时加入假阳性抑制算法和曲线拟合技术，实现了较高的检测准确率和每秒 411 帧的推理速度。此外，在使用 TensorRT 优化后的 Nvidia Jetson AGX Xavier 嵌入式系统上也可以实时检测车道，其推理速度为每秒 56 帧。

SwiftLane: 快速高效车道检测

SwiftLane: Towards Fast and Efficient Lane Detection

We apply machine learning to the problem of finding numerical Calabi-Yau
metrics. Building on Donaldson's algorithm for calculating balanced metrics on
K\"ahler manifolds, we combine conventional curve fitting and machine-learning
techniques to numerically approximate Ricci-flat metrics. We show that machine
learning is able to predict the Calabi-Yau metric and quantities associated
with it, such as its determinant, having seen only a small sample of training
data. Using this in conjunction with a straightforward curve fitting routine,
we demonstrate that it is possible to find highly accurate numerical metrics
much more quickly than by using Donaldson's algorithm alone, with our new
machine-learning algorithm decreasing the time required by between one and two
orders of magnitude.

该研究使用机器学习方法进行数值 Calabi-Yau 度量的寻找，结合曲线拟合与机器学习技术，相比于传统算法，在保持精度的情况下，将时间成本进一步缩减了一至两个数量级。

机器学习 Calabi-Yau 度量

Machine learning Calabi-Yau metrics

We present an approach that improves the sample complexity for a variety of
curve fitting problems, including active learning for linear regression,
polynomial regression, and continuous sparse Fourier transforms. In the active
linear regression problem, one would like to estimate the least squares
solution $\beta^*$ minimizing $\|X\beta - y\|_2$ given the entire unlabeled
dataset $X \in \mathbb{R}^{n \times d}$ but only observing a small number of
labels $y_i$. We show that $O(d)$ labels suffice to find a constant factor
approximation $\tilde{\beta}$:
\[
\mathbb{E}[\|X\tilde{\beta} - y\|_2^2] \leq 2 \mathbb{E}[\|X \beta^* -
y\|_2^2].
\] This improves on the best previous result of $O(d \log d)$ from leverage
score sampling. We also present results for the \emph{inductive} setting,
showing when $\tilde{\beta}$ will generalize to fresh samples; these apply to
continuous settings such as polynomial regression. Finally, we show how the
techniques yield improved results for the non-linear sparse Fourier transform
setting.

本研究提出了一种方法，通过减小样本复杂度，提高了各种曲线拟合问题包括线性回归、多项式回归和连续稀疏傅里叶变换的活性学习中的解的精度。