Protein classification tasks are essential in drug discovery. Real-world
protein structures are dynamic, which will determine the properties of
proteins. However, the existing machine learning methods, like ProNet (Wang et
al., 2022a), only access limited conformational characteristics and protein
side-chain features, leading to impractical protein structure and inaccuracy of
protein classes in their predictions. In this paper, we propose novel semantic
data augmentation methods, Novel Augmentation of New Node Attributes (NaNa),
and Molecular Interactions and Geometric Upgrading (MiGu) to incorporate
backbone chemical and side-chain biophysical information into protein
classification tasks and a co-embedding residual learning framework.
Specifically, we leverage molecular biophysical, secondary structure, chemical
bonds, and ionic features of proteins to facilitate protein classification
tasks. Furthermore, our semantic augmentation methods and the co-embedding
residual learning framework can improve the performance of GIN (Xu et al.,
2019) on EC and Fold datasets (Bairoch, 2000; Andreeva et al., 2007) by 16.41%
and 11.33% respectively. Our code is available at
this https URL

提出一种新型语义数据增强方法和共嵌入残差学习框架，以将蛋白质的主链化学和侧链生物物理信息纳入蛋白质分类任务中，并使 GIN 在 EC 和 Fold 数据集上的性能分别提高了 16.41% 和 11.33%。

NaNa 和 MiGu：用于增强图神经网络中蛋白质分类的语义数据增强技术

NaNa and MiGu: Semantic Data Augmentation Techniques to Enhance Protein  Classification in Graph Neural Networks

Fully test-time adaptation (FTTA) adapts a model that is trained on a source
domain to a target domain during the testing phase, where the two domains
follow different distributions and source data is unavailable during the
training phase. Existing methods usually adopt entropy minimization to reduce
the uncertainty of target prediction results, and improve the FTTA performance
accordingly. However, they fail to ensure the diversity in target prediction
results. Recent domain adaptation study has shown that maximizing the sum of
singular values of prediction results can simultaneously enhance their
confidence (discriminability) and diversity. However, during the training
phase, larger singular values usually take up a dominant position in loss
maximization. This results in the model being more inclined to enhance
discriminability for easily distinguishable classes, and the improvement in
diversity is insufficiently effective. Furthermore, the adaptation and
prediction in FTTA only use data from the current batch, which may lead to the
risk of overfitting. To address the aforementioned issues, we propose
maximizing the sum of singular values while minimizing their variance. This
enables the model's focus toward the smaller singular values, enhancing
discriminability between more challenging classes and effectively increasing
the diversity of prediction results. Moreover, we incorporate data from the
previous batch to realize semantic data augmentation for the current batch,
reducing the risk of overfitting. Extensive experiments on benchmark datasets
show our proposed approach outperforms some compared state-of-the-art FTTA
methods.

通过最大化奇异值的总和同时最小化其方差，我们提出的方法在实现 FTTA 过程中增强了较难区分的类别的判别能力，有效提高了预测结果的多样性，并通过使用前一批次的数据为当前批次实现语义数据增广以减小过拟合风险。广泛的基准实验表明了我们提出的方法相较于某些先进的 FTTA 方法取得了更好的性能。

奇异值惩罚和语义数据增强的全面测试时间自适应

Singular Value Penalization and Semantic Data Augmentation for Fully  Test-Time Adaptation

Human pose estimation is the task of localizing body keypoints from still
images. The state-of-the-art methods suffer from insufficient examples of
challenging cases such as symmetric appearance, heavy occlusion and nearby
person. To enlarge the amounts of challenging cases, previous methods augmented
images by cropping and pasting image patches with weak semantics, which leads
to unrealistic appearance and limited diversity. We instead propose Semantic
Data Augmentation (SDA), a method that augments images by pasting segmented
body parts with various semantic granularity. Furthermore, we propose
Adversarial Semantic Data Augmentation (ASDA), which exploits a generative
network to dynamiclly predict tailored pasting configuration. Given
off-the-shelf pose estimation network as discriminator, the generator seeks the
most confusing transformation to increase the loss of the discriminator while
the discriminator takes the generated sample as input and learns from it. The
whole pipeline is optimized in an adversarial manner. State-of-the-art results
are achieved on challenging benchmarks.

提出了基于语义数据增强和对抗学习的人体姿势估计方法，在应对外观对称性、严重遮挡和近距离干扰等多种挑战性情况下，取得了最先进的结果。

对抗性语义数据增强用于人体姿态估计

Adversarial Semantic Data Augmentation for Human Pose Estimation

Data augmentation is widely known as a simple yet surprisingly effective
technique for regularizing deep networks. Conventional data augmentation
schemes, e.g., flipping, translation or rotation, are low-level,
data-independent and class-agnostic operations, leading to limited diversity
for augmented samples. To this end, we propose a novel semantic data
augmentation algorithm to complement traditional approaches. The proposed
method is inspired by the intriguing property that deep networks are effective
in learning linearized features, i.e., certain directions in the deep feature
space correspond to meaningful semantic transformations, e.g., changing the
background or view angle of an object. Based on this observation, translating
training samples along many such directions in the feature space can
effectively augment the dataset for more diversity. To implement this idea, we
first introduce a sampling based method to obtain semantically meaningful
directions efficiently. Then, an upper bound of the expected cross-entropy (CE)
loss on the augmented training set is derived by assuming the number of
augmented samples goes to infinity, yielding a highly efficient algorithm. In
fact, we show that the proposed implicit semantic data augmentation (ISDA)
algorithm amounts to minimizing a novel robust CE loss, which adds minimal
extra computational cost to a normal training procedure. In addition to
supervised learning, ISDA can be applied to semi-supervised learning tasks
under the consistency regularization framework, where ISDA amounts to
minimizing the upper bound of the expected KL-divergence between the augmented
features and the original features. Although being simple, ISDA consistently
improves the generalization performance of popular deep models (e.g., ResNets
and DenseNets) on a variety of datasets, i.e., CIFAR-10, CIFAR-100, SVHN,
ImageNet, and Cityscapes.

提出一种基于深度神经网络学习的语义数据增强方法 (ISDA), 通过此方法可以提高卷积神经网络在图像分类任务上的泛化性能.