Crystallographic Symmetry for Data Augmentation in Detecting Dendrite Cores

Accurately and rapidly detecting the locations of the cores of large-scale dendrites from 2D sectioned microscopic images helps quantify the microstructure of material components. This provides a critical link between the processing and properties of the material. Such a tool could be a critical part of a quality control procedure for manufacturing these components. In this paper, we propose to use Faster R-CNN, a convolutional neural network (CNN) model that considers both the detection accuracy and computational efficiency, to detect the dendrite cores with complex shapes. However, training CNN models usually requires a large number of images annotated with ground-truth locations of dendrite cores, which are usually obtained by highly laborintensive manual annotations. In this paper, we leverage the crystallographic symmetry of dendrite cores for data augmentation – the cross sections of dendrite cores show, not perfect, but near four-fold rotation symmetry and we can rotate the image around the center of dendrite cores by specified angles to construct new training data without additional manual annotations. We conduct a series of experiments and the results show the effectiveness of the Faster R-CNN method with the proposed data augmentation strategy. Particularly, we find that we can reduce the number of the manually annotated training images by 75% while still maintaining the same detection accuracy of dendrite cores.

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Gravity Control-Based Data Augmentation Technique for Improving VR User Activity Recognition

Symmetry ◽

10.3390/sym13050845 ◽

2021 ◽

Vol 13 (5) ◽

pp. 845

Author(s):

Dongheun Han ◽

Chulwoo Lee ◽

Hyeongyeop Kang

Keyword(s):

Activity Recognition ◽

Large Scale ◽

Data Augmentation ◽

Training Data ◽

Measurement Unit ◽

Gravitational Acceleration ◽

The Neural Network ◽

Typical Data ◽

Robust Recognition ◽

Gravity Acceleration

The neural-network-based human activity recognition (HAR) technique is being increasingly used for activity recognition in virtual reality (VR) users. The major issue of a such technique is the collection large-scale training datasets which are key for deriving a robust recognition model. However, collecting large-scale data is a costly and time-consuming process. Furthermore, increasing the number of activities to be classified will require a much larger number of training datasets. Since training the model with a sparse dataset can only provide limited features to recognition models, it can cause problems such as overfitting and suboptimal results. In this paper, we present a data augmentation technique named gravity control-based augmentation (GCDA) to alleviate the sparse data problem by generating new training data based on the existing data. The benefits of the symmetrical structure of the data are that it increased the number of data while preserving the properties of the data. The core concept of GCDA is two-fold: (1) decomposing the acceleration data obtained from the inertial measurement unit (IMU) into zero-gravity acceleration and gravitational acceleration, and augmenting them separately, and (2) exploiting gravity as a directional feature and controlling it to augment training datasets. Through the comparative evaluations, we validated that the application of GCDA to training datasets showed a larger improvement in classification accuracy (96.39%) compared to the typical data augmentation methods (92.29%) applied and those that did not apply the augmentation method (85.21%).

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Feature Re-Learning for Video Recommendation

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35350 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 3143-3149

Author(s):

Chanjal C

Keyword(s):

Data Augmentation ◽

Video Retrieval ◽

Poor Performance ◽

Training Data ◽

Video Annotation ◽

Video Copy Detection ◽

Copy Detection ◽

Augmentation Strategy ◽

Ranking Loss ◽

Video Recommendation

Predicting the relevance between two given videos with respect to their visual content is a key component for content-based video recommendation and retrieval. The application is in video recommendation, video annotation, Category or near-duplicate video retrieval, video copy detection and so on. In order to estimate video relevance previous works utilize textual content of videos and lead to poor performance. The proposed method is feature re-learning for video relevance prediction. This work focus on the visual contents to predict the relevance between two videos. A given feature is projected into a new space by an affine transformation. Different from previous works this use a standard triplet ranking loss that optimize the projection process by a novel negative-enhanced triplet ranking loss. In order to generate more training data, propose a data augmentation strategy which works directly on video features. The multi-level augmentation strategy works for video features, which benefits the feature relearning. The proposed augmentation strategy can be flexibly used for frame-level or video-level features. The loss function that consider the absolute similarity of positive pairs and supervise the feature re-learning process and a new formula for video relevance computation.

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Using Vehicle Synthesis Generative Adversarial Networks to Improve Vehicle Detection in Remote Sensing Images

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8090390 ◽

2019 ◽

Vol 8 (9) ◽

pp. 390 ◽

Cited By ~ 1

Author(s):

Kun Zheng ◽

Mengfei Wei ◽

Guangmin Sun ◽

Bilal Anas ◽

Yu Li

Keyword(s):

Remote Sensing ◽

Traffic Control ◽

Data Augmentation ◽

Vehicle Detection ◽

Training Data ◽

Generative Adversarial Networks ◽

Remote Sensing Images ◽

Adversarial Networks ◽

Vehicle Data ◽

Augmentation Strategy

Vehicle detection based on very high-resolution (VHR) remote sensing images is beneficial in many fields such as military surveillance, traffic control, and social/economic studies. However, intricate details about the vehicle and the surrounding background provided by VHR images require sophisticated analysis based on massive data samples, though the number of reliable labeled training data is limited. In practice, data augmentation is often leveraged to solve this conflict. The traditional data augmentation strategy uses a combination of rotation, scaling, and flipping transformations, etc., and has limited capabilities in capturing the essence of feature distribution and proving data diversity. In this study, we propose a learning method named Vehicle Synthesis Generative Adversarial Networks (VS-GANs) to generate annotated vehicles from remote sensing images. The proposed framework has one generator and two discriminators, which try to synthesize realistic vehicles and learn the background context simultaneously. The method can quickly generate high-quality annotated vehicle data samples and greatly helps in the training of vehicle detectors. Experimental results show that the proposed framework can synthesize vehicles and their background images with variations and different levels of details. Compared with traditional data augmentation methods, the proposed method significantly improves the generalization capability of vehicle detectors. Finally, the contribution of VS-GANs to vehicle detection in VHR remote sensing images was proved in experiments conducted on UCAS-AOD and NWPU VHR-10 datasets using up-to-date target detection frameworks.

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Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.6085 ◽

2020 ◽

Vol 34 (04) ◽

pp. 6194-6201

Author(s):

Jing Wang ◽

Weiqing Min ◽

Sujuan Hou ◽

Shengnan Ma ◽

Yuanjie Zheng ◽

...

Keyword(s):

Image Recognition ◽

Real World ◽

Large Scale ◽

Data Augmentation ◽

Ground Truth ◽

Classification Task ◽

The Real ◽

Product Recommendation ◽

Contextual Advertising ◽

Benchmark Datasets

Logo classification has gained increasing attention for its various applications, such as copyright infringement detection, product recommendation and contextual advertising. Compared with other types of object images, the real-world logo images have larger variety in logo appearance and more complexity in their background. Therefore, recognizing the logo from images is challenging. To support efforts towards scalable logo classification task, we have curated a dataset, Logo-2K+, a new large-scale publicly available real-world logo dataset with 2,341 categories and 167,140 images. Compared with existing popular logo datasets, such as FlickrLogos-32 and LOGO-Net, Logo-2K+ has more comprehensive coverage of logo categories and larger quantity of logo images. Moreover, we propose a Discriminative Region Navigation and Augmentation Network (DRNA-Net), which is capable of discovering more informative logo regions and augmenting these image regions for logo classification. DRNA-Net consists of four sub-networks: the navigator sub-network first selected informative logo-relevant regions guided by the teacher sub-network, which can evaluate its confidence belonging to the ground-truth logo class. The data augmentation sub-network then augments the selected regions via both region cropping and region dropping. Finally, the scrutinizer sub-network fuses features from augmented regions and the whole image for logo classification. Comprehensive experiments on Logo-2K+ and other three existing benchmark datasets demonstrate the effectiveness of proposed method. Logo-2K+ and the proposed strong baseline DRNA-Net are expected to further the development of scalable logo image recognition, and the Logo-2K+ dataset can be found at https://github.com/msn199959/Logo-2k-plus-Dataset.

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Attention-Aware Adversarial Network for Person Re-Identification

Applied Sciences ◽

10.3390/app9081550 ◽

2019 ◽

Vol 9 (8) ◽

pp. 1550 ◽

Cited By ~ 1

Author(s):

Aihong Shen ◽

Huasheng Wang ◽

Junjie Wang ◽

Hongchen Tan ◽

Xiuping Liu ◽

...

Keyword(s):

High Performance ◽

Large Scale ◽

Data Augmentation ◽

Fundamental Problem ◽

Training Data ◽

Specific Data ◽

Training Strategy ◽

Adversarial Network ◽

Benchmark Datasets ◽

Adversarial Training

Person re-identification (re-ID) is a fundamental problem in the field of computer vision. The performance of deep learning-based person re-ID models suffers from a lack of training data. In this work, we introduce a novel image-specific data augmentation method on the feature map level to enforce feature diversity in the network. Furthermore, an attention assignment mechanism is proposed to enforce that the person re-ID classifier focuses on nearly all important regions of the input person image. To achieve this, a three-stage framework is proposed. First, a baseline classification network is trained for person re-ID. Second, an attention assignment network is proposed based on the baseline network, in which the attention module learns to suppress the response of the current detected regions and re-assign attentions to other important locations. By this means, multiple important regions for classification are highlighted by the attention map. Finally, the attention map is integrated in the attention-aware adversarial network (AAA-Net), which generates high-performance classification results with an adversarial training strategy. We evaluate the proposed method on two large-scale benchmark datasets, including Market1501 and DukeMTMC-reID. Experimental results show that our algorithm performs favorably against the state-of-the-art methods.

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SaltSeg: Automatic 3D salt segmentation using a deep convolutional neural network

Interpretation ◽

10.1190/int-2018-0235.1 ◽

2019 ◽

Vol 7 (3) ◽

pp. SE113-SE122 ◽

Cited By ~ 26

Author(s):

Yunzhi Shi ◽

Xinming Wu ◽

Sergey Fomel

Keyword(s):

Large Scale ◽

Model Building ◽

Ground Truth ◽

Velocity Model ◽

Training Data ◽

Data Sets ◽

Validation Data ◽

Data Set ◽

Seismic Image ◽

Data Generator

Salt boundary interpretation is important for the understanding of salt tectonics and velocity model building for seismic migration. Conventional methods consist of computing salt attributes and extracting salt boundaries. We have formulated the problem as 3D image segmentation and evaluated an efficient approach based on deep convolutional neural networks (CNNs) with an encoder-decoder architecture. To train the model, we design a data generator that extracts randomly positioned subvolumes from large-scale 3D training data set followed by data augmentation, then feed a large number of subvolumes into the network while using salt/nonsalt binary labels generated by thresholding the velocity model as ground truth labels. We test the model on validation data sets and compare the blind test predictions with the ground truth. Our results indicate that our method is capable of automatically capturing subtle salt features from the 3D seismic image with less or no need for manual input. We further test the model on a field example to indicate the generalization of this deep CNN method across different data sets.

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Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM

10.1101/330548 ◽

2018 ◽

Cited By ~ 1

Author(s):

Germán Abrevaya ◽

Aleksandr Aravkin ◽

Guillermo Cecchi ◽

Irina Rish ◽

Pablo Polosecki ◽

...

Keyword(s):

Large Scale ◽

Data Augmentation ◽

Predictive Accuracy ◽

Training Data ◽

Van Der Pol Oscillator ◽

Brain Dynamics ◽

Optimization Approach ◽

Imaging Data ◽

Van Der Pol ◽

Temporal Models

AbstractMany real-world data sets, especially in biology, are produced by highly multivariate and nonlinear complex dynamical systems. In this paper, we focus on brain imaging data, including both calcium imaging and functional MRI data. Standard vector-autoregressive models are limited by their linearity assumptions, while nonlinear general-purpose, large-scale temporal models, such as LSTM networks, typically require large amounts of training data, not always readily available in biological applications; furthermore, such models have limited interpretability. We introduce here a novel approach for learning a nonlinear differential equation model aimed at capturing brain dynamics. Specifically, we propose a variable-projection optimization approach to estimate the parameters of the multivariate (coupled) van der Pol oscillator, and demonstrate that such a model can accurately represent nonlinear dynamics of the brain data. Furthermore, in order to improve the predictive accuracy when forecasting future brain-activity time series, we use this analytical model as an unlimited source of simulated data for pretraining LSTM; such model-specific data augmentation approach consistently improves LSTM performance on both calcium and fMRI imaging data.

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Data set entity recognition based on distant supervision

The Electronic Library ◽

10.1108/el-10-2020-0301 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Pengcheng Li ◽

Qikai Liu ◽

Qikai Cheng ◽

Wei Lu

Keyword(s):

Supervised Learning ◽

Large Scale ◽

Data Augmentation ◽

Scientific Literature ◽

Neural Model ◽

Training Data ◽

Entity Recognition ◽

Data Set ◽

Content Type ◽

Augmentation Techniques

Purpose This paper aims to identify data set entities in scientific literature. To address poor recognition caused by a lack of training corpora in existing studies, a distant supervised learning-based approach is proposed to identify data set entities automatically from large-scale scientific literature in an open domain. Design/methodology/approach Firstly, the authors use a dictionary combined with a bootstrapping strategy to create a labelled corpus to apply supervised learning. Secondly, a bidirectional encoder representation from transformers (BERT)-based neural model was applied to identify data set entities in the scientific literature automatically. Finally, two data augmentation techniques, entity replacement and entity masking, were introduced to enhance the model generalisability and improve the recognition of data set entities. Findings In the absence of training data, the proposed method can effectively identify data set entities in large-scale scientific papers. The BERT-based vectorised representation and data augmentation techniques enable significant improvements in the generality and robustness of named entity recognition models, especially in long-tailed data set entity recognition. Originality/value This paper provides a practical research method for automatically recognising data set entities in scientific literature. To the best of the authors’ knowledge, this is the first attempt to apply distant learning to the study of data set entity recognition. The authors introduce a robust vectorised representation and two data augmentation strategies (entity replacement and entity masking) to address the problem inherent in distant supervised learning methods, which the existing research has mostly ignored. The experimental results demonstrate that our approach effectively improves the recognition of data set entities, especially long-tailed data set entities.

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StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications

Symmetry ◽

10.3390/sym13081497 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1497

Author(s):

Harold Achicanoy ◽

Deisy Chaves ◽

Maria Trujillo

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Data Augmentation ◽

Industrial Applications ◽

Generative Models ◽

Training Data ◽

Generative Adversarial Networks ◽

Augmentation Strategy ◽

Synthetic Images ◽

The Impact

Deep learning applications on computer vision involve the use of large-volume and representative data to obtain state-of-the-art results due to the massive number of parameters to optimise in deep models. However, data are limited with asymmetric distributions in industrial applications due to rare cases, legal restrictions, and high image-acquisition costs. Data augmentation based on deep learning generative adversarial networks, such as StyleGAN, has arisen as a way to create training data with symmetric distributions that may improve the generalisation capability of built models. StyleGAN generates highly realistic images in a variety of domains as a data augmentation strategy but requires a large amount of data to build image generators. Thus, transfer learning in conjunction with generative models are used to build models with small datasets. However, there are no reports on the impact of pre-trained generative models, using transfer learning. In this paper, we evaluate a StyleGAN generative model with transfer learning on different application domains—training with paintings, portraits, Pokémon, bedrooms, and cats—to generate target images with different levels of content variability: bean seeds (low variability), faces of subjects between 5 and 19 years old (medium variability), and charcoal (high variability). We used the first version of StyleGAN due to the large number of publicly available pre-trained models. The Fréchet Inception Distance was used for evaluating the quality of synthetic images. We found that StyleGAN with transfer learning produced good quality images, being an alternative for generating realistic synthetic images in the evaluated domains.

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DAISM-DNN: Highly accurate cell type proportion estimation with in silico data augmentation and deep neural networks

10.1101/2020.03.26.009308 ◽

2020 ◽

Author(s):

Yating Lin ◽

Haojun Li ◽

Xu Xiao ◽

Wenxian Yang ◽

Rongshan Yu

Keyword(s):

Neural Networks ◽

In Silico ◽

Deep Neural Networks ◽

Data Augmentation ◽

Immune Cell ◽

Ground Truth ◽

Training Data ◽

Cancer Treatments ◽

Proportion Estimation ◽

Real World Datasets

Understanding the immune-cell abundances of cancer and other disease-related tissues has an important role in guiding cancer treatments. We propose data augmentation through in silico mixing with deep neural networks (DAISM-DNN), where highly accurate and unbiased immune-cell proportion estimation is achieved through DNN with dataset-specific training data created from partial samples from the same batch with ground truth cell proportions. We evaluated the performance of DAISM-DNN on three publicly available real-world datasets and results showed that DAISM-DNN is robust against platform-specific variations among different datasets and outperforms other existing methods by a significant margin on all the datasets evaluated.

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