Automotive radars have an important role in autonomous driving systems. The
main challenge in automotive radar detection is the radar's wide point spread
function (PSF) in the angular domain that causes blurriness and clutter in the
radar image. Numerous studies suggest employing an 'end-to-end' learning
strategy using a Deep Neural Network (DNN) to directly detect objects from
radar images. This approach implicitly addresses the PSF's impact on objects of
interest. In this paper, we propose an alternative approach, which we term
"Boosting Radar Reflections" (BoostRad). In BoostRad, a first DNN is trained to
narrow the PSF for all the reflection points in the scene. The output of the
first DNN is a boosted reflection image with higher resolution and reduced
clutter, resulting in a sharper and cleaner image. Subsequently, a second DNN
is employed to detect objects within the boosted reflection image. We develop a
novel method for training the boosting DNN that incorporates domain knowledge
of radar's PSF characteristics. BoostRad's performance is evaluated using the
RADDet and CARRADA datasets, revealing its superiority over reference methods.

提出了一种名为 “Boosting Radar Reflections”（BoostRad）的替代方法，该方法通过首先训练一个神经网络来减小车载雷达在场景中反射点的点扩散函数（PSF），从而得到一个分辨率更高且噪声更少的图像，然后再通过第二个神经网络来检测增强后的反射图像中的目标物体，并通过对雷达 PSF 特性的领域知识融入训练过程来优化 BoostRad 的性能。在 RADDet 和 CARRADA 数据集的评估中，证明了 BoostRad 相比参考方法的优越性。

BoostRad: 提升雷达反射对象检测

BoostRad: Enhancing Object Detection by Boosting Radar Reflections

Lack of interpretability of deep convolutional neural networks (DCNN) is a
well-known problem particularly in the medical domain as clinicians want
trustworthy automated decisions. One way to improve trust is to demonstrate the
localisation of feature representations with respect to expert labeled regions
of interest. In this work, we investigate the localisation of features learned
via two varied learning paradigms and demonstrate the superiority of one
learning approach with respect to localisation. Our analysis on medical and
natural datasets show that the traditional end-to-end (E2E) learning strategy
has a limited ability to localise discriminative features across multiple
network layers. We show that a layer-wise learning strategy, namely cascade
learning (CL), results in more localised features. Considering localisation
accuracy, we not only show that CL outperforms E2E but that it is a promising
method of predicting regions. On the YOLO object detection framework, our best
result shows that CL outperforms the E2E scheme by $2\%$ in mAP.

通过两种不同的学习模式来探究通过特征定位的方式学习到的特征，在医学和自然数据集上的分析表明了层级学习策略的优越性，本研究显示级联学习是一种有前景的区域预测方法，并在 YOLO 目标检测框架上显示出比端到端方案提升了 2% 的 mAP。

级联学习用于视觉场景分类任务中的特征定位

Cascade Learning Localises Discriminant Features in Visual Scene  Classification

A rising vision for AI in the open world centers on the development of
systems that can complement humans for perceptual, diagnostic, and reasoning
tasks. To date, systems aimed at complementing the skills of people have
employed models trained to be as accurate as possible in isolation. We
demonstrate how an end-to-end learning strategy can be harnessed to optimize
the combined performance of human-machine teams by considering the distinct
abilities of people and machines. The goal is to focus machine learning on
problem instances that are difficult for humans, while recognizing instances
that are difficult for the machine and seeking human input on them. We
demonstrate in two real-world domains (scientific discovery and medical
diagnosis) that human-machine teams built via these methods outperform the
individual performance of machines and people. We then analyze conditions under
which this complementarity is strongest, and which training methods amplify it.
Taken together, our work provides the first systematic investigation of how
machine learning systems can be trained to complement human reasoning.

本文针对开放环境中的 AI，通过使用端到端学习策略，结合人类和机器的不同能力组成的人机团队优化其组合性能，并在科学发现和医疗诊断领域进行了实证研究，结果表明表现优于单独的机器和人类认知，是对机器学习如何辅助人类推理的系统性研究。