Data augmentation has been proven effective for training high-accuracy
convolutional neural network classifiers by preventing overfitting. However,
building deep neural networks in real-world scenarios requires not only high
accuracy on clean data but also robustness when data distributions shift. While
prior methods have proposed that there is a trade-off between accuracy and
robustness, we propose IPMix, a simple data augmentation approach to improve
robustness without hurting clean accuracy. IPMix integrates three levels of
data augmentation (image-level, patch-level, and pixel-level) into a coherent
and label-preserving technique to increase the diversity of training data with
limited computational overhead. To further improve the robustness, IPMix
introduces structural complexity at different levels to generate more diverse
images and adopts the random mixing method for multi-scale information fusion.
Experiments demonstrate that IPMix outperforms state-of-the-art corruption
robustness on CIFAR-C and ImageNet-C. In addition, we show that IPMix also
significantly improves the other safety measures, including robustness to
adversarial perturbations, calibration, prediction consistency, and anomaly
detection, achieving state-of-the-art or comparable results on several
benchmarks, including ImageNet-R, ImageNet-A, and ImageNet-O.

IPMix 提供了一种简单且可用于提高神经网络的鲁棒性的数据增强方法，通过整合图像级、补丁级和像素级三层数据增强，并引入结构复杂性和随机混合方法以扩大训练数据的多样性，实现在多个评估参数上的优越性能。

IPMix：用于训练鲁棒分类器的标签保持数据增强方法

IPMix: Label-Preserving Data Augmentation Method for Training Robust  Classifiers

Systematic generalization is a crucial aspect of intelligence, which refers
to the ability to generalize to novel tasks by combining known subtasks and
concepts. One critical factor that has been shown to influence systematic
generalization is the diversity of training data. However, diversity can be
defined in various ways, as data have many factors of variation. A more
granular understanding of how different aspects of data diversity affect
systematic generalization is lacking. We present new evidence in the problem of
Visual Question Answering (VQA) that reveals that the diversity of simple tasks
(i.e. tasks formed by a few subtasks and concepts) plays a key role in
achieving systematic generalization. This implies that it may not be essential
to gather a large and varied number of complex tasks, which could be costly to
obtain. We demonstrate that this result is independent of the similarity
between the training and testing data and applies to well-known families of
neural network architectures for VQA (i.e. monolithic architectures and neural
module networks). Additionally, we observe that neural module networks leverage
all forms of data diversity we evaluated, while monolithic architectures
require more extensive amounts of data to do so. These findings provide a first
step towards understanding the interactions between data diversity design,
neural network architectures, and systematic generalization capabilities.

通过研究可视化问答问题，我们发现培训数据多样性对于实现系统化概括至关重要，而简单任务的多样性在实现系统化概括方面起着关键作用。同时，我们观察到神经模块网络利用了我们评估的所有数据多样性形式，而整体架构需要更多的数据来实现相同的效果。这些发现对于理解数据多样性设计、神经网络架构和系统化概括能力之间的相互作用提供了初步的认识。

D3：面向视觉问答中系统化概括的数据多样性设计

D3: Data Diversity Design for Systematic Generalization in Visual  Question Answering

As an effective strategy, data augmentation (DA) alleviates data scarcity
scenarios where deep learning techniques may fail. It is widely applied in
computer vision then introduced to natural language processing and achieves
improvements in many tasks. One of the main focuses of the DA methods is to
improve the diversity of training data, thereby helping the model to better
generalize to unseen testing data. In this survey, we frame DA methods into
three categories based on the diversity of augmented data, including
paraphrasing, noising, and sampling. Our paper sets out to analyze DA methods
in detail according to the above categories. Further, we also introduce their
applications in NLP tasks as well as the challenges. Some helpful resources are
provided in the appendix.

本文综述了数据增强的三个类别：释义、加噪和采样，以及在 NLP 中的应用和挑战。