Null Model-Based Data Augmentation for Graph Classification

Deep learning has recently been applied to automatically classify the modulation categories of received radio signals without manual experience. However, training deep learning models requires massive volume of data. An insufficient training data will cause serious overfitting problem and degrade the classification accuracy. To cope with small dataset, data augmentation has been widely used in image processing to expand the dataset and improve the robustness of deep learning models. However, in wireless communication areas, the effect of different data augmentation methods on radio modulation classification has not been studied yet. In this paper, we evaluate different data augmentation methods via a state-of-the-art deep learning-based modulation classifier. Based on the characteristics of modulated signals, three augmentation methods are considered, i.e., rotation, flip, and Gaussian noise, which can be applied in both training phase and inference phase of the deep learning algorithm. Numerical results show that all three augmentation methods can improve the classification accuracy. Among which, the rotation augmentation method outperforms the flip method, both of which achieve higher classification accuracy than the Gaussian noise method. Given only 12.5% of training dataset, a joint rotation and flip augmentation policy can achieve even higher classification accuracy than the baseline with initial 100% training dataset without augmentation. Furthermore, with data augmentation, radio modulation categories can be successfully classified using shorter radio samples, leading to a simplified deep learning model and shorter the classification response time.

The CutMix technique is a sophisticated approach for augmenting data in order to train neural network-based image classifiers. Essentially, it involves cutting out a portion of a random image and pasting it into the same location as another image. However, because of the irregularity of graph data, CutMix cannot be directly applied to graph learning. Our paper introduces G-CutMix, a CutMix-based data augmentation approach that we designed specifically for bot detection in social media networks. G-CutMix involves conducting CutMix operations between the original graph and a shuffled graph, which precedes the graph convolution process. The outputs of the graph convolution are then strategically merged with the user representations from both the original and shuffled graphs. Our proposed G-CutMix not only leverages the power of graph convolutions but also introduces a layer of complexity that mimics real-world scenarios where bot behavior can be subtle and varied, making G-CutMix a formidable tool in the arsenal against bot detection. Our experiments confirm that our approach can consistently enhance the performance of bot detection across various GNN architectures, including Graph Convolutional Networks, GraphSAGE, and Graph Attention Networks.

Intelligent fault diagnosis is an important method in rotating machinery fault diagnosis and equipment health management. To deal with co-frequency vibration faults, a type of typical fault in rotating machinery, this paper proposes a fault diagnosis method based on the stacked autoencoder (SAE) and ensembled ResNet-SVM. Furthermore, the time- and frequency-domain features of several co-frequency vibration faults are summarized based on the mechanism analysis and calculated using actual vibration data. To realize and validate the high-precision diagnosis method of rotating equipment with co-frequency faults proposed in this study, the following three criteria are required: First, to improve the effectiveness and robustness of the ensembled model and the sliding window using data augmentation, adding noise, autoencoder (AE) and SAE methods are analyzed in terms of principle and practical effects. Second, ResNet is used as the feature extractor for the ensembled ResNet-SVM model. Feature extraction is carried out twice, and the extracted co-frequency fault features are more comprehensive. Finally, the data augmentation method and ensemble ResNet-SVM are combined for fault diagnosis and compared with other methods. The experimental results show that the accuracy of the proposed method can exceed 99.9%.

The rapid serial visual presentation-based brain–computer interface (RSVP-BCI) system achieves the recognition of target images by extracting event-related potential (ERP) features from electroencephalogram (EEG) signals and then building target classification models. Currently, how to reduce the training and calibration time for classification models across different subjects is a crucial issue in the practical application of RSVP. To address this issue, a zero-calibration (ZC) method termed Attention-ProNet, which involves meta-learning with a prototype network integrating multiple attention mechanisms, was proposed in this study. In particular, multiscale attention mechanisms were used for efficient EEG feature extraction. Furthermore, a hybrid attention mechanism was introduced to enhance model generalization, and attempts were made to incorporate suitable data augmentation and channel selection methods to develop an innovative and high-performance ZC RSVP-BCI decoding model algorithm. The experimental results demonstrated that our method achieved a balance accuracy (BA) of 86.33% in the decoding task for new subjects. Moreover, appropriate channel selection and data augmentation methods further enhanced the performance of the network by affording an additional 2.3% increase in BA. The model generated by the meta-learning prototype network Attention-ProNet, which incorporates multiple attention mechanisms, allows for the efficient and accurate decoding of new subjects without the need for recalibration or retraining.

Combinations of data augmentation methods and deep learning architectures for automatic pancreas segmentation on CT images are proposed and evaluated. Images from a public CT dataset of pancreas segmentation were used to evaluate the models. Baseline U-net and deep U-net were chosen for the deep learning models of pancreas segmentation. Methods of data augmentation included conventional methods, mixup, and random image cropping and patching (RICAP). Ten combinations of the deep learning models and the data augmentation methods were evaluated. Four-fold cross validation was performed to train and evaluate these models with data augmentation methods. The dice similarity coefficient (DSC) was calculated between automatic segmentation results and manually annotated labels and these were visually assessed by two radiologists. The performance of the deep U-net was better than that of the baseline U-net with mean DSC of 0.703–0.789 and 0.686–0.748, respectively. In both baseline U-net and deep U-net, the methods with data augmentation performed better than methods with no data augmentation, and mixup and RICAP were more useful than the conventional method. The best mean DSC was obtained using a combination of deep U-net, mixup, and RICAP, and the two radiologists scored the results from this model as good or perfect in 76 and 74 of the 82 cases.

Data augmentation (DA) increases the diversity of training data to improve the model generalization ability. Most of the DA methods focus on image classification or object detection. Directly using the existing DA methods on change detection tasks not only falls short of fully exploring the specificity of the change image pairs but also leads to longer training times. In this article, we first propose a mask-guided mixing (MGM) DA for change detection, which mixes the change regions of the current training sample based on prediction results and labels to generate high-quality samples with more positive samples. We then propose a new reinforcement learning (RL)-based Adaptive DA method, AdaAug+, to adaptively select the optimal DA policy for the training samples. An actor selects the best augmentation operation from the operation set according to the image pair. The augmented image pairs make it easier for the change detector to learn the optimal parameters and improve the final detection performance. To reduce the training time, we identify and remove the redundant training samples during the training process by our redundancy searching policy. We have conducted various experiments on four remote sensing change detection datasets with different change detectors. The experimental results demonstrate that AdaAug+ achieves promising performance compared to the state-of-the-art DA methods and requires less training time.

A problem with convolutional neural networks (CNNs) is that they require large datasets to obtain adequate robustness; on small datasets, they are prone to overfitting. Many methods have been proposed to overcome this shortcoming with CNNs. In cases where additional samples cannot easily be collected, a common approach is to generate more data points from existing data using an augmentation technique. In image classification, many augmentation approaches utilize simple image manipulation algorithms. In this work, we propose some new methods for data augmentation based on several image transformations: the Fourier transform (FT), the Radon transform (RT), and the discrete cosine transform (DCT). These and other data augmentation methods are considered in order to quantify their effectiveness in creating ensembles of neural networks. The novelty of this research is to consider different strategies for data augmentation to generate training sets from which to train several classifiers which are combined into an ensemble. Specifically, the idea is to create an ensemble based on a kind of bagging of the training set, where each model is trained on a different training set obtained by augmenting the original training set with different approaches. We build ensembles on the data level by adding images generated by combining fourteen augmentation approaches, with three based on FT, RT, and DCT, proposed here for the first time. Pretrained ResNet50 networks are finetuned on training sets that include images derived from each augmentation method. These networks and several fusions are evaluated and compared across eleven benchmarks. Results show that building ensembles on the data level by combining different data augmentation methods produce classifiers that not only compete competitively against the state-of-the-art but often surpass the best approaches reported in the literature.

This dataset is an extended version of from the aboved paper. The dataset contains more than 500,000 legal arguments which were automatically labeled by combining the following algorithms with majority vote:\n\n\n\tFine-Tuned Legal BERT on training data involving few Paraphrased test samples. Accuracy: 0.834933 \n\tFine-Tuned Legal BERT with custom algorithm involving a corruption of few train samples. Accuracy: 0.76775 \n\tFine-Tuned Legal BERT Accuracy: 0.76199 \n\tFine-Tuned BERT Accuracy: 0.76007 \n\tGAN-BERT Accuracy: 0.7480\n\tCo Training Accuracy: 0.66410 \n\tEM-LightGBM Accuracy: 0.6564 \n\tPseudo Labeling Accuracy: 0.6339 \n\n\nThere are six labels in the dataset: 1) fact, 2) issue, 3)rule/law/holding, 4) analysis, 5) conclusion/opinion/answer, and 6) others. For more details about the labels and the algorithms, please read our paper on: IEEE Transactions on Reliability

With global warming, increasingly severe forest fires have caused serious damage to the Earth’s ecological environment. Remote sensing (RS) technology has become an important means of forest fire monitoring owing to its unique advantages of multiple cycles and wide coverage. In smoke scene detection, the current simple mixed sample data augmentation (MSDA) methods lead to the loss of key objects in images, and training the samples whose images do not match the labels is the main reason for the decline in classifier accuracy. We propose a Class Activation Map-based Mixing method (CAMMix), a new MSDA method. In contrast to previous data augmentation (DA) methods, CAMMix can select the region and degree of mixing throughout the significance map. With the aid of an auxiliary classifier, CAMMix generates a mixed mask with class significance, such that the newly generated mixed sample distribution is close to the original data distribution. We also propose an intervention for the coverage method to further prevent the loss of smoke objects. On a smoke dataset (USTC_SmokeRS), CAMMix achieves the best accuracy of 94.95% and 83% for 64% and 8% training set respectively. It results that CAMMix and variants provides high classification accuracy and preserves key object information, outperforming the Mixup, CutMix, ResizeMix and FMix.

Data augmentation is widely utilized as an effective technique to enhance the generalization performance of deep models. However, data augmentation may inevitably introduce distribution shifts and noises, which significantly constrain the potential and deteriorate the performance of deep networks. To this end, we propose a novel information-preserving framework, namely IPF-RDA, to enhance the robustness of data augmentations in this paper. IPF-RDA combines the proposal of (i) a new class-discriminative information estimation algorithm that identifies the points most vulnerable to data augmentation operations and corresponding importance scores; And (ii) a new information-preserving scheme that preserves the critical information in the augmented samples and ensures the diversity of augmented data adaptively. We divide data augmentation methods into three categories according to the operation types and integrate these approaches into our framework accordingly. After being integrated into our framework, the robustness of data augmentation methods can be enhanced and their full potential can be unleashed. Extensive experiments demonstrate that although being simple, IPF-RDA consistently improves the performance of numerous commonly used state-of-the-art data augmentation methods with popular deep models on a variety of datasets, including CIFAR-10, CIFAR-100, Tiny-ImageNet, CUHK03, Market1501, Oxford Flower, and MNIST, where its performance and scalability are stressed.

Pedestrian detection plays a crucial role in autonomous driving by identifying the position, size, orientation, and dynamic features of pedestrians in images or videos, assisting autonomous vehicles in making better decisions and controls. It’s worth noting that the performance of pedestrian detection models largely depends on the quality and diversity of available training data. Current datasets for autonomous driving have limitations in terms of diversity, scale, and quality. In recent years, numerous studies have proposed the use of data augmentation strategies to expand the coverage of datasets, aiming to maximize the utilization of existing training data. However, these data augmentation methods often overlook the diversity of data scenarios. To overcome this challenge, in this paper, we propose a more comprehensive method for data augmentation, based on image descriptions and diffusion models. This method aims to cover a wider range of scene variations, including different weather conditions and lighting situations. We have designed a classifier to select data samples for augmentation, followed by extracting visual features based on image captions and converting them into high-level semantic information as textual descriptions for the corresponding samples. Finally, we utilize diffusion models to generate new variants. Additionally, we have designed three modification patterns to increase diversity in aspects such as weather conditions, lighting, and pedestrian poses within the data. We conducted extensive experiments on the KITTI dataset and in real-world environments, demonstrating that our proposed method significantly enhances the performance of pedestrian detection models in complex scenarios. This meticulous consideration of data augmentation will notably enhance the applicability and robustness of pedestrian detection models in actual autonomous driving scenarios.

A Method of Data Augmentation for Classifying Road Damage Considering Influence on Classification Accuracy

Recently, developing more accurate and more efficient deep learning algorithms for medical images segmentation attracts more and more attentions of researchers. Most of methods increase the depth of the network to replace with acquiring multi-information. The costs of training images annotation are too expensive to label by hand. In this paper, we propose a multi-scale and better performance deep architecture for medical image segmentation, named Res2-UNeXt. Our architecture is an encoder-decoder network with Res2XBlocks. The Res2XBlocks aim at acquiring multi-scale information better in images. To cooperate with Res2-UNeXt, we put forward a simple and efficient method of data augmentation. The data augmentation method, based on the process of cell movement and deformation, has biological implications in away. We evaluate Res2-UNeXt in comparison with recent variants of U-Net: UNet++, CE-Net and LadderNet and the method that different from U-Net architecture: FCN and DFANet on the dataset of ISBI cell tracking challenge 2019 via four different cell images. The experimental results demonstrate that Res2-UNeXt can achieve better performance than both recent variants of U-Net and non-U-Net architecture methods. Besides, the proposed architecture and the data augmentation method have been proven efficiently by the ablation experiments.

Optical character recognition is a technology that turns texts and scanned documents into digital formats. The practical applications of OCR systems face a lot of challenges because of heterogeneity in scripts, font styles, and different quality images arising during their usage. The proposed work attempts to improve the robustness of OCR models built for the Hindi language by addressing the deficiencies inherent in the widely used IIIT-HW-Dev dataset. Toward this end, two methods of data augmentation are proposed: 1) synthetic images with half characters and conjuncts for complex words, and 2) various image degradations to simulate the real world. The augmented training set is used to train the two deep architectures, namely Convolutional Recurrent Neural Networks (CRNNs) and ResNet-50. Comparisons and in-depth experiments further reveal the merits of the suggested data augmentation techniques: the top performance obtained by the CRNN model exceeds current approaches. On the test set, CRNN achieved a Character Error Rate (CER) of 2.14% and Word Error Rate (WER) of 7.96%, much beyond the CER of 3.27% and WER of 11.82% achieved by the ResNet-50 model. Qualitatively, there was robustness in the case of complex words and images that were also deteriorated. The augmented dataset and trained models can be downloaded by anyone to promote more research in Hindi OCR. So, developing robust OCR systems for complex scripts starts from variability-rich and representative training data along with specific architectures, such as the CRNN architecture.

Discriminative feature constraints via supervised contrastive learning for few-shot forest tree species classification using airborne hyperspectral images