In recent years, software systems powered by deep learning (DL) techniques
have significantly facilitated people's lives in many aspects. As the backbone
of these DL systems, various DL libraries undertake the underlying optimization
and computation. However, like traditional software, DL libraries are not
immune to bugs, which can pose serious threats to users' personal property and
safety. Studying the characteristics of DL libraries, their associated bugs,
and the corresponding testing methods is crucial for enhancing the security of
DL systems and advancing the widespread application of DL technology. This
paper provides an overview of the testing research related to various DL
libraries, discusses the strengths and weaknesses of existing methods, and
provides guidance and reference for the application of the DL library. This
paper first introduces the workflow of DL underlying libraries and the
characteristics of three kinds of DL libraries involved, namely DL framework,
DL compiler, and DL hardware library. It then provides definitions for DL
underlying library bugs and testing. Additionally, this paper summarizes the
existing testing methods and tools tailored to these DL libraries separately
and analyzes their effectiveness and limitations. It also discusses the
existing challenges of DL library testing and outlines potential directions for
future research.

深度学习技术在各个领域极大地方便了人们的生活，但深度学习库作为这些系统的支柱，同样会受到漏洞的威胁。本文提供了与深度学习库相关的测试研究概述，讨论了现有方法的优势、劣势，并为深度学习库的应用提供指导和参考，同时还总结了针对这些库的现有测试方法和工具，分析了其有效性和局限性，并探讨了深度学习库测试的挑战和未来的研究方向。

深度学习库测试方法概述

A Survey of Deep Learning Library Testing Methods

Providing online adaptive lightweight time series anomaly detection without
human intervention and domain knowledge is highly valuable. Several such
anomaly detection approaches have been introduced in the past years, but all of
them were only implemented in one deep learning library. With the development
of deep learning libraries, it is unclear how different deep learning libraries
impact these anomaly detection approaches since there is no such evaluation
available. Randomly choosing a deep learning library to implement an anomaly
detection approach might not be able to show the true performance of the
approach. It might also mislead users in believing one approach is better than
another. Therefore, in this paper, we investigate the impact of deep learning
libraries on online adaptive lightweight time series anomaly detection by
implementing two state-of-the-art anomaly detection approaches in three
well-known deep learning libraries and evaluating how these two approaches are
individually affected by the three deep learning libraries. A series of
experiments based on four real-world open-source time series datasets were
conducted. The results provide a good reference to select an appropriate deep
learning library for online adaptive lightweight anomaly detection.

本文研究使用不同的深度学习库实现两种先进的异常检测方法，探究深度学习库对于轻量级时间序列异常检测的影响，并通过实验证明其影响，从而提供一个选择合适深度学习库的参考。

深度学习库对在线自适应轻量级时间序列异常检测的影响

Impact of Deep Learning Libraries on Online Adaptive Lightweight Time  Series Anomaly Detection

We propose an efficient and unified framework, namely ThiNet, to
simultaneously accelerate and compress CNN models in both training and
inference stages. We focus on the filter level pruning, i.e., the whole filter
would be discarded if it is less important. Our method does not change the
original network structure, thus it can be perfectly supported by any
off-the-shelf deep learning libraries. We formally establish filter pruning as
an optimization problem, and reveal that we need to prune filters based on
statistics information computed from its next layer, not the current layer,
which differentiates ThiNet from existing methods. Experimental results
demonstrate the effectiveness of this strategy, which has advanced the
state-of-the-art. We also show the performance of ThiNet on ILSVRC-12
benchmark. ThiNet achieves 3.31$\times$ FLOPs reduction and 16.63$\times$
compression on VGG-16, with only 0.52$\%$ top-5 accuracy drop. Similar
experiments with ResNet-50 reveal that even for a compact network, ThiNet can
also reduce more than half of the parameters and FLOPs, at the cost of roughly
1$\%$ top-5 accuracy drop. Moreover, the original VGG-16 model can be further
pruned into a very small model with only 5.05MB model size, preserving AlexNet
level accuracy but showing much stronger generalization ability.

ThiNet 是一种快速压缩 CNN 模型的框架，通过统计信息计算从下一层获取关键信息来剪枝，可实现 FLOPs 减少和模型压缩。实验结果表明，即使对于紧凑网络，ThiNet 也能减少一半以上的参数和 FLOPs，牺牲不到 1% 的精度。