A self-supervised data augmentation strategy for EEG-based emotion recognition

In agriculture, pest always causes the major damage in fields and results in significant crop yield losses. Currently, manual pest classification and counting are very time-consuming and many subjective factors can affect the population counting accuracy. In addition, the existing pest localization and recognition methods based on Convolutional Neural Network (CNN) are not satisfactory for practical pest prevention in fields because of pests’ different scales and attitudes. In order to address these problems, an effective data augmentation strategy for CNN-based method is proposed in this paper. In training phase, we adopt data augmentation through rotating images by various degrees followed by cropping into different grids. In this way, we could obtain a large number of extra multi-scale examples that could be adopted to train a multi-scale pest detection model. In terms of test phase, we utilize the test time augmentation (TTA) strategy that separately inferences input images with various resolutions using the trained multi-scale model. Finally, we fuse these detection results from different image scales by non-maximum suppression (NMS) for the final result. Experimental results on wheat sawfly, wheat aphid, wheat mite and rice planthopper in our domain specific dataset, show that our proposed data augmentation strategy achieves the pest detection performance of 81.4% mean Average Precision (mAP), which improves 11.63%, 7.93%,4.73% compared to three state-of-the-art approaches.

Emotion recognition constitutes a pivotal research topic within affective computing, owing to its potential applications across various domains. Currently, emotion recognition methods based on deep learning frameworks utilizing electroencephalogram (EEG) signals have demonstrated effective application and achieved impressive performance. However, in EEG-based emotion recognition, there exists a significant performance drop in cross-subject EEG Emotion recognition due to inter-individual differences among subjects. In order to address this challenge, a hybrid transfer learning strategy is proposed, and the Domain Adaptation with a Few-shot Fine-tuning Network (DFF-Net) is designed for cross-subject EEG emotion recognition. The first step involves the design of a domain adaptive learning module specialized for EEG emotion recognition, known as the Emo-DA module. Following this, the Emo-DA module is utilized to pre-train a model on both the source and target domains. Subsequently, fine-tuning is performed on the target domain specifically for the purpose of cross-subject EEG emotion recognition testing. This comprehensive approach effectively harnesses the attributes of domain adaptation and fine-tuning, resulting in a noteworthy improvement in the accuracy of the model for the challenging task of cross-subject EEG emotion recognition. The proposed DFF-Net surpasses the state-of-the-art methods in the cross-subject EEG emotion recognition task, achieving an average recognition accuracy of 93.37% on the SEED dataset and 82.32% on the SEED-IV dataset.

One-shot segmentation of novel white matter tracts via extensive data augmentation and adaptive knowledge transfer.

An improved common spatial pattern combined with channel-selection strategy for electroencephalography-based emotion recognition

DAAL-WS: A weakly-supervised method integrated with data augmentation and active learning strategies for MLS point cloud semantic segmentation

Residual Gabor convolutional network and FV-Mix exponential level data augmentation strategy for finger vein recognition

Emotion recognition is the task of recognizing a person’s emotional state. EEG, as a physiological signal, can provide more detailed and complex information for emotion recognition task. Meanwhile, EEG can’t be changed and hidden intentionally makes EEG-based emotion recognition achieve more effective and reliable result. Unfortunately, due to the cost of data collection, most EEG datasets have small number of EEG data. The lack of data makes it difficult to predict the emotion states with the deep models, which requires enough number of training data. In this paper, we propose to use a simple data augmentation method to address the issue of data shortage in EEG-based emotion recognition. In experiments, we explore the performance of emotion recognition with the shallow and deep computational models before and after data augmentation on two standard EEG-based emotion datasets. Our experimental results show that the simple data augmentation method can improve the performance of emotion recognition based on deep models effectively.

Synthetic datasets alleviate the shortage of label data in the real world to some extent. But, synthetic datasets still have problems with the complexity of picture backgrounds and text diversity. It is well known that collecting large amounts of real data is a job that requires a lot of human resources and material resources. Therefore, we propose a small batch data augmentation strategy, hoping to improve significant performance by collecting and labeling small batches of real data. We have verified our ideas on a strong baseline. The result shows that the accuracy of the model can be significantly improved by replacing the synthetic dataset with the real dataset, which proved that real datasets could train the model better than synthetic datasets. Then, we use different enhancement strategies to expand the data of small batches of real data sets and explore the performance improvement of the model under the condition of low-resource real data. Finally, we mixed the augmented small batch of real datasets and synthetic datasets to make the model learn the image features of real scenes more elegantly. The results show that the proposed strategy can well fill the gap between synthetic and real datasets and improve the model performance.

Data augmentation for medical imaging: A systematic literature review

A supervised data augmentation strategy based on random combinations of key features

The recognition of postearthquake scenes plays an important role in postearthquake rescue and reconstruction. To overcome the over-reliance on expert visual interpretation and the poor recognition performance of traditional machine learning in postearthquake scene recognition, this paper proposes a postearthquake multiple scene recognition (PEMSR) model based on the classical deep learning Single Shot MultiBox Detector (SSD) method. In this paper, a labeled postearthquake scenes dataset is constructed by segmenting acquired remote sensing images, which are classified into six categories: landslide, houses, ruins, trees, clogged and ponding. Due to the insufficiency and imbalance of the original dataset, transfer learning and a data augmentation and balancing strategy are utilized in the PEMSR model. To evaluate the PEMSR model, the evaluation metrics of precision, recall and F1 score are used in the experiment. Multiple experimental test results demonstrate that the PEMSR model shows a stronger performance in postearthquake scene recognition. The PEMSR model improves the detection accuracy of each scene compared with SSD by transfer learning and data augmentation strategy. In addition, the average detection time of the PEMSR model only needs 0.4565s, which is far less than the 8.3472s of the traditional Histogram of Oriented Gradient + Support Vector Machine (HOG+SVM) method.

Development of convolutional neural networks for recognition of tenogenic differentiation based on cellular morphology

Large Language Models (LLMs) can be applied to many fields in the display industry. However, general LLMs lack domain‐specific knowledge and specialized terminology understanding, which results in inaccurate responses when applied to industrial question‐answering(Q&A) scenarios. To address this issue, this work introduces a framework of Large Language Model training to effectively import the Display Industry Knowledge. This framework is specifically designed to enhance the comprehension ability of LLMs on the knowledge from the display industry field by improving specialized data governance, knowledge distillation techniques, data augmentation strategies, and continual pre‐training mechanisms. This approach not only significantly improves the model's performance in Q&A applications within the display industry but also prevents catastrophic forgetting of common knowledge. Experimental results demonstrate the effectiveness of these techniques. We hope that this work can be also helpful for the customization of LLMs in other specialized domains.

Objective. The data scarcity problem in emotion recognition from electroencephalography (EEG) leads to difficulty in building an affective model with high accuracy using machine learning algorithms or deep neural networks. Inspired by emerging deep generative models, we propose three methods for augmenting EEG training data to enhance the performance of emotion recognition models. Approach. Our proposed methods are based on two deep generative models, variational autoencoder (VAE) and generative adversarial network (GAN), and two data augmentation ways, full and partial usage strategies. For the full usage strategy, all of the generated data are augmented to the training dataset without judging the quality of the generated data, while for the partial usage, only high-quality data are selected and appended to the training dataset. These three methods are called conditional Wasserstein GAN (cWGAN), selective VAE (sVAE), and selective WGAN (sWGAN). Main results. To evaluate the effectiveness of these proposed methods, we perform a systematic experimental study on two public EEG datasets for emotion recognition, namely, SEED and DEAP. We first generate realistic-like EEG training data in two forms: power spectral density and differential entropy. Then, we augment the original training datasets with a different number of generated realistic-like EEG data. Finally, we train support vector machines and deep neural networks with shortcut layers to build affective models using the original and augmented training datasets. The experimental results demonstrate that our proposed data augmentation methods based on generative models outperform the existing data augmentation approaches such as conditional VAE, Gaussian noise, and rotational data augmentation. We also observe that the number of generated data should be less than 10 times of the original training dataset to achieve the best performance. Significance. The augmented training datasets produced by our proposed sWGAN method significantly enhance the performance of EEG-based emotion recognition models.

Affective media videos have been used as stimulus to investigate an individual's affective-physio responses. In this study, we aim to develop a network learning strategy for robust cross-corpus emotion recognition using physiological features jointly with affective video content. Specifically, we present a novel framework of Visual Semantic Graph Learning Convolutional Network (VGLCN) for individual emotional state recognition using physiology on transfer learning tasks. The stimulus of videos content is integrated into learnable graph structure to weight the importance of physiology on the two emotion dimensions, valence and arousal. Furthermore, we evaluate our proposed framework on two public emotion databases with a rigorous cross validation method, and our model achieves the best unweighted average recall (UAR), which is 67.9%, 56.9% for arousal and 79.8%, 70.4% for valence on the cross datasets recognition experiments respectively. Further analyses reveal that 1) VGLCN is especially effective on transfer valence binary-task, 2) the physiological features (ECG, EDA) are very informative features for emotion recognition and 3) the affective media videos are important constraint to be included in the framework to stabilize the performance power.