The field of eXplainable Artificial Intelligence (XAI) has greatly advanced
in recent years, but progress has mainly been made in computer vision and
natural language processing. For time series, where the input is often not
interpretable, only limited research on XAI is available. In this work, we put
forward a virtual inspection layer, that transforms the time series to an
interpretable representation and allows to propagate relevance attributions to
this representation via local XAI methods like layer-wise relevance propagation
(LRP). In this way, we extend the applicability of a family of XAI methods to
domains (e.g. speech) where the input is only interpretable after a
transformation. Here, we focus on the Fourier transformation which is
prominently applied in the interpretation of time series and LRP and refer to
our method as DFT-LRP. We demonstrate the usefulness of DFT-LRP in various time
series classification settings like audio and electronic health records. We
showcase how DFT-LRP reveals differences in the classification strategies of
models trained in different domains (e.g., time vs. frequency domain) or helps
to discover how models act on spurious correlations in the data.

本文介绍了一种虚拟检查层，可以将时间序列转换为可解释的表示形式，使得 XAI 的方法可以适用于仅在转换后才能被解释的领域，如语音，同时演示了 DFT-LRP 在各种时间序列分类设置中的应用，并显示了 DFT-LRP 如何显示出不同分类策略的区别。

基于虚拟检查层的时序可解释人工智能

Explainable AI for Time Series via Virtual Inspection Layers

The topic of generalizing machine learning models learned on a collection of
source domains to unknown target domains is challenging. While many domain
generalization (DG) methods have achieved promising results, they primarily
rely on the source domains at train-time without manipulating the target
domains at test-time. Thus, it is still possible that those methods can overfit
to source domains and perform poorly on target domains. Driven by the
observation that domains are strongly related to styles, we argue that reducing
the gap between source and target styles can boost models' generalizability. To
solve the dilemma of having no access to the target domain during training, we
introduce Test-time Fourier Style Calibration (TF-Cal) for calibrating the
target domain style on the fly during testing. To access styles, we utilize
Fourier transformation to decompose features into amplitude (style) features
and phase (semantic) features. Furthermore, we present an effective technique
to Augment Amplitude Features (AAF) to complement TF-Cal. Extensive experiments
on several popular DG benchmarks and a segmentation dataset for medical images
demonstrate that our method outperforms state-of-the-art methods.

通过使用傅里叶变换将特征分解为振幅（样式）特征和相位（语义）特征，并引入测试时傅里叶样式校准（TF-Cal）来校准目标域风格，以增强模型的泛化能力。实验表明，该方法在几个流行的 DG 基准测试和医学图像分割数据集中优于最先进的方法。

领域泛化的测试时傅里叶风格校准

Test-time Fourier Style Calibration for Domain Generalization

The goal of this paper is to create a new framework for dense SLAM that is
light enough for micro-robot systems based on depth camera and inertial sensor.
Feature-based and direct methods are two mainstreams in visual SLAM. Both
methods minimize photometric or reprojection error by iterative solutions,
which are computationally expensive. To overcome this problem, we propose a
non-iterative framework to reduce computational requirement. First, the
attitude and heading reference system (AHRS) and axonometric projection are
utilized to decouple the 6 Degree-of-Freedom (DoF) data, so that point clouds
can be matched in independent spaces respectively. Second, based on single
key-frame training, the matching process is carried out in frequency domain by
Fourier transformation, which provides a closed-form non-iterative solution. In
this manner, the time complexity is reduced to $\mathcal{O}(n \log{n})$, where
$n$ is the number of matched points in each frame. To the best of our
knowledge, this method is the first non-iterative and online trainable approach
for data association in visual SLAM. Compared with the state-of-the-arts, it
runs at a faster speed and obtains 3-D maps with higher resolution yet still
with comparable accuracy.

本论文提出了一种基于深度相机和惯性传感器的微型机器人系统的新型密集 SLAM 的框架，使用非迭代的 Fourier 变换来减少计算需求，通过解耦 6 自由度数据和进行独立空间点云匹配，实现分类键帧训练以及数据关联，相较现有方法，其速度更快，分辨率更高，但精度相当。