Explore the vast range of titles available.

In many cases of machine learning, research suggests that the development of training data might have a higher relevance than the choice and modelling of classifiers themselves. Thus, data augmentation methods have been developed to improve classifiers by artificially created training data. In NLP, there is the challenge of establishing universal rules for text transformations which provide new linguistic patterns. In this paper, we present and evaluate a text generation method suitable to increase the performance of classifiers for long and short texts. We achieved promising improvements when evaluating short as well as long text tasks with the enhancement by our text generation method. Especially with regard to small data analytics, additive accuracy gains of up to 15.53% and 3.56% are achieved within a constructed low data regime, compared to the no augmentation baseline and another data augmentation technique. As the current track of these constructed regimes is not universally applicable, we also show major improvements in several real world low data tasks (up to +4.84 F1-score). Since we are evaluating the method from many perspectives (in total 11 datasets), we also observe situations where the method might not be suitable. We discuss implications and patterns for the successful application of our approach on different types of datasets.

Image data augmentation constitutes a critical methodology in modern computer vision tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; thereby, improving the performance and robustness of machine learning models in downstream tasks. In parallel, augmentation approaches can also be used for editing/modifying a given image in a context- and semantics-aware way. Diffusion Models (DMs), which comprise one of the most recent and highly promising classes of methods in the field of generative Artificial Intelligence (AI), have emerged as a powerful tool for image data augmentation, capable of generating realistic and diverse images by learning the underlying data distribution. The current study realizes a systematic, comprehensive and in-depth review of DM-based approaches for image augmentation, covering a wide range of strategies, tasks and applications. In particular, a comprehensive analysis of the fundamental principles, model architectures and training strategies of DMs is initially performed. Subsequently, a taxonomy of the relevant image augmentation methods is introduced, focusing on techniques regarding semantic manipulation, personalization and adaptation, and application-specific augmentation tasks. Then, performance assessment methodologies and respective evaluation metrics are analyzed. Finally, current challenges and future research directions in the field are discussed.

In recent years, one of the most popular techniques in the computer vision community has been the deep learning technique. As a data-driven technique, deep model requires enormous amounts of accurately labelled training data, which is often inaccessible in many real-world applications. A data-space solution is Data Augmentation (DA), that can artificially generate new images out of original samples. Image augmentation strategies can vary by dataset, as different data types might require different augmentations to facilitate model training. However, the design of DA policies has been largely decided by the human experts with domain knowledge, which is considered to be highly subjective and error-prone. To mitigate such problem, a novel direction is to automatically learn the image augmentation policies from the given dataset using Automated Data Augmentation (AutoDA) techniques. The goal of AutoDA models is to find the optimal DA policies that can maximize the model performance gains. This survey discusses the underlying reasons of the emergence of AutoDA technology from the perspective of image classification. We identify three key components of a standard AutoDA model: a search space, a search algorithm and an evaluation function. Based on their architecture, we provide a systematic taxonomy of existing image AutoDA approaches. This paper presents the major works in AutoDA field, discussing their pros and cons, and proposing several potential directions for future improvements.

The analysis of sleep stages for children plays an important role in early diagnosis and treatment. This paper introduces our sleep stage classification method addressing the following two challenges: the first is the data imbalance problem, i.e., the highly skewed class distribution with underrepresented minority classes. For this, a Gaussian Noise Data Augmentation (GNDA) algorithm was applied to polysomnography recordings to seek the balance of data sizes for different sleep stages. The second challenge is the difficulty in identifying a minority class of sleep stages, given their short sleep duration and similarities to other stages in terms of EEG characteristics. To overcome this, we developed a DeConvolution- and Self-Attention-based Model (DCSAM) which can inverse the feature map of a hidden layer to the input space to extract local features and extract the correlations between all possible pairs of features to distinguish sleep stages. The results on our dataset show that DCSAM based on GNDA obtains an accuracy of 90.26% and a macro F1-score of 86.51% which are higher than those of our previous method. We also tested DCSAM on a well-known public dataset—Sleep-EDFX—to prove whether it is applicable to sleep data from adults. It achieves a comparable performance to state-of-the-art methods, especially accuracies of 91.77%, 92.54%, 94.73%, and 95.30% for six-stage, five-stage, four-stage, and three-stage classification, respectively. These results imply that our DCSAM based on GNDA has a great potential to offer performance improvements in various medical domains by considering the data imbalance problems and correlations among features in time series data.

In the aerospace industry, the Automated Fiber Placement process is an established method for producing composite parts. Nowadays the required visual inspection, subsequent to this process, typically takes up to 50% of the total manufacturing time and the inspection quality strongly depends on the inspector. A Deep Learning based classification of manufacturing defects is a possibility to improve the process efficiency and accuracy. However, these techniques require several hundreds or thousands of training data samples. Acquiring this huge amount of data is difficult and time consuming in a real world manufacturing process. Thus, an approach for augmenting a smaller number of defect images for the training of a neural network classifier is presented. Five traditional methods and eight deep learning approaches are theoretically assessed according to the literature. The selected conditional Deep Convolutional Generative Adversarial Network and Geometrical Transformation techniques are investigated in detail, with regard to the diversity and realism of the synthetic images. Between 22 and 166 laser line scan sensor images per defect class from six common fiber placement inspection cases are utilised for tests. The GAN-Train GAN-Test method was applied for the validation. The studies demonstrated that a conditional Deep Convolutional Generative Adversarial Network combined with a previous Geometrical Transformation is well suited to generate a large realistic data set from less than 50 actual input images. The presented network architecture and the associated training weights can serve as a basis for applying the demonstrated approach to other fibre layup inspection images.

Autonomous driving requires robust and highly accurate perception technologies. Various deep learning algorithms based on only image processing satisfy this requirement, but few such algorithms are based on LiDAR. However, images are only one part of the perceptible sensors in an autonomous driving vehicle; LiDAR is also essential for the recognition of driving environments. The main reason why there exist few deep learning algorithms based on LiDAR is a lack of data. Recent translation technology using generative adversarial networks (GANs) has been proposed to deal with this problem. However, these technologies focus on only image-to-image translation, although a lack of data occurs more often with LiDAR than with images. LiDAR translation technology is required not only for data augmentation, but also for driving simulation, which allows algorithms to practice driving as if they were commanding a real vehicle, before doing so in the real world. In other words, driving simulation is a key technology for evaluating and verifying algorithms which are practically applied to vehicles. In this paper, we propose a GAN-based LiDAR translation algorithm for autonomous driving and driving simulation. It is the first LiDAR translation approach that can deal with various types of weather that are based on an empirical approach. We tested the proposed method on the JARI data set, which was collected under various adverse weather scenarios with diverse precipitation and visible distance settings. The proposed method was also applied to the real-world Spain data set. Our experimental results demonstrate that the proposed method can generate realistic LiDAR data under adverse weather conditions.

With the continuous development of human life and society, the medical field is constantly improving. However, modern medicine still faces many limitations, including challenging and previously unsolvable problems. In these cases, artificial intelligence (AI) can provide solutions. The research and application of generative adversarial networks (GAN) are a clear example. While most researchers focus on image augmentation, there are few one-dimensional data augmentation examples. The radiomics feature extracted from RT and CT images is one-dimensional data. As far as we know, we are the first to apply the WGAN-GP algorithm to generate radiomics data in the medical field. In this paper, we input a portion of the original real data samples into the model. The model learns the distribution of the input data samples and generates synthetic data samples with similar distribution to the original real data, which can solve the problem of obtaining annotated medical data samples. We have conducted experiments on the public dataset Heart Disease Cleveland and the private dataset. Compared with the traditional method of Synthetic Minority Oversampling Technique (SMOTE) and common GAN for data augmentation, our method has significantly improved the AUC and SEN values under different data proportions. At the same time, our method has also shown varying levels of improvement in ACC and SPE values. This demonstrates that our method is effective and feasible.

Before deploying a monocular depth estimation (MDE) model in real-world applications such as autonomous driving, it is critical to understand its generalization and robustness. Although the generalization of MDE models has been thoroughly studied, the robustness of the models has been overlooked in previous research. Existing state-of-the-art methods exhibit strong generalization to clean, unseen scenes. Such methods, however, appear to degrade when the test image is perturbed. This is likely because the prior arts typically use the primary 2D data augmentations (e.g., random horizontal flipping, random cropping, and color jittering), ignoring other common image degradation or corruptions. To mitigate this issue, we delve deeper into data augmentation and propose utilizing strong data augmentation techniques for robust depth estimation. In particular, we introduce 3D-aware defocus blur in addition to seven 2D data augmentations. We evaluate the generalization of our model on six clean RGB-D datasets that were not seen during training. To evaluate the robustness of MDE models, we create a benchmark by applying 15 common corruptions to the clean images from IBIMS, NYUDv2, KITTI, ETH3D, DIODE, and TUM. On this benchmark, we systematically study the robustness of our method and 9 representative MDE models. The experimental results demonstrate that our model exhibits better generalization and robustness than the previous methods. Specifically, we provide valuable insights about the choices of data augmentation strategies and network architectures, which would be useful for future research in robust monocular depth estimation. Our code, model, and benchmark can be available at https://github.com/KexianHust/Robust-MonoDepth.

Developing an automatic speaker verification (ASV) system for children’s speech presents significant challenges. One major obstacle is the scarcity of domain-specific data. This issue is exacerbated when dealing with short speech utterances, a relatively unexplored area in children’s ASV. Voice biometric systems struggle during enrollment and verification phase, when faced with inadequate speech data, both in volume as well as in duration. To address data scarcity, this paper explores various in-domain and out-of-domain data augmentation techniques. Out-of-domain data from adult speakers, which have distinct acoustic attributes from children, are modified using techniques like voice-conversion, prosody and formant modification to make them acoustically similar to children’s speech. In-domain data augmentation involves perturbing the speed of children’s speech. This combined data augmentation approach not only increases training data volume but also captures missing target attributes, resulting in a significant 43.91% reduction in equal error rate (EER) compared to the baseline system. Additionally, the paper addresses the challenge of preserving higher-frequency components in children’s speech. It achieves this by concatenating conventional Mel-frequency cepstral coefficients (MFCC) with Inverse-Mel-frequency cepstral coefficient (IMFCC) features at the frame level. The low canonical correlation between MFCC and IMFCC feature vectors motivates this fusion. The feature concatenation approach, when combined with proposed data augmentation, results in an appreciable reduction of 48.51% in the overall EER, demonstrating its effectiveness in improving the performance of children’s ASV system.

COVID-19 pandemic has fueled the interest in artificial intelligence tools for quick diagnosis to limit virus spreading. Over 60% of people who are infected complain of a dry cough. Cough and other respiratory sounds were used to build diagnosis models in much recent research. We propose in this work, an augmentation pipeline which is applied on the pre-filtered data and uses i) pitch-shifting technique to augment the raw signal and, ii) spectral data augmentation technique SpecAugment to augment the computed mel-spectrograms. A deep learning based architecture that hybridizes convolution neural networks and long-short term memory with an attention mechanism is proposed for building the classification model. The feasibility of the proposed is demonstrated through a set of testing scenarios using the large-scale COUGHVID cough dataset and through a comparison with three baselines models. We have shown that our classification model achieved 91.13% of testing accuracy, 90.93% of sensitivity and an area under the curve of receiver operating characteristic of 91.13%.