In contrast, the plot on the right shows a model with the desired relationship between training and testing error. The reduced CIFAR-10 results demonstrate the usefulness of the SamplePairing technique in limited data applications (Fig. Another interesting alternative to Reinforcement Learning is simple random search [112]. 2018. The impressive performance of GANs has resulted in increased attention on how they can be applied to the task of Data Augmentation. Mingyang G, Kele X, Bo D, Huaimin W, Lei Z. The synthetic data produced the training data by enumerating through different fonts and augmentations. 2016. Int Soc Opt Photonics. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Berlin: Springer; 2013. p.11724. 2017;542:1158. A survey on Image Data Augmentation for Deep Learning Buda et al. Their original dataset contains 182 CT scans, (53 Cysts, 64 Metastases, and 65 Hemangiomas). The real or fake dichotomy is analogous to whether or not the generated instance is from the training set or if it was created by the generator network (Fig. Noise injection is tested by Moreno-Barea et al. 23). Article Data Augmentation is similar to imagination or dreaming. [84] presented DisturbLabel, a regularization technique that randomly replaces labels at each iteration. 2017;42:60-88. . Adding noise to images can help CNNs learn more robust features. Data Augmentation is not limited to the image domain and can be useful for text, bioinformatics, tabular records, and many more. Goodfellow et al. Architecture diagram of the feature space augmentation framework presented by DeVries and Taylor [75], Examples of interpolated instances in the feature space on the handwritten @ character [75]. In like manner, Liang et al. An obvious disadvantage of this technique is that it makes little sense from a human perspective. This encompasses augmentations such as geometric and color transformations, random erasing, adversarial training, and neural style transfer. These augmentations are valuable for strengthening weak spots in the classification model. Gregory K, Richard Z, Ruslan S. Siamese neural networks for one-shot image recognition. An interesting question for practical Data Augmentation is how to determine post-augmented dataset size. Additionally another loss function is incorporated into Network-A to ensure that its outputs are similar to others within the class. By augmenting images in advance, the distributed system is able to request and pre-cache training batches. In this study, we perform a comprehensive survey of image augmentation for deep learning using a novel informative taxonomy. 5, 6). Guotai W, Michael A, Sebastien O, Wenqi L, Jan D, Tom V. Test-time augmentation with uncertainty estimation for deep learning-based medical image segmentation. This is done under the assumption that more information can be extracted from the original dataset through augmentations. arXiv preprint. In the design of a massively distributed training system, Chilimbi et al. Adversarial augmentations may not represent examples likely to occur in the test set, but they can improve weak spots in the learned decision boundary. The image augmentation algorithms discussed in this survey include geometric transformations, color space augmentations, kernel filters, mixing images, random erasing, feature space augmentation . In: Proceedings of the empirical methods in natural language processing (EMNLP 2014) 12. In: International conference on learning representations (ICLR); 2017. [39] cover the use of GAN image synthesis in medical imaging applications such as brain MRI synthesis [44, 45], lung cancer diagnosis [46], high-resolution skin lesion synthesis [47], and chest x-ray abnormality classification [48]. However, for some tasks, color is a very important distinctive feature. [125]. 2016. [109, 110] also show the effectiveness of evolutionary algorithms for architecture search. Google Scholar. If the initial training dataset consists of 50 dogs and 50 cats, and each image is augmented with 100 color filters to produce 5000 dogs and 5000 cats, this dataset will be heavily biased towards the spatial characteristics of the original 50 dogs and 50 cats. 2018;10574:105741. 2023 BioMed Central Ltd unless otherwise stated. The combinations of flipping+cropping and flipping+WGAN were the best overall, improving classification performance on CIFAR-10 by +3% and +3.5%, respectively. Mehdi M, Simon O. 26). [6]. Data Augmentation for Deep Graph Learning: A Survey They tested these augmentations with 4-fold cross-validation on the Caltech101 dataset filtered to 8421 images of size 256256 (Table1). Jia D, Wei D, Richard S, Li-Jia L, Kai L, Li F-F. ImageNet: a large-scale hierarchical image database. Improved regularization of convolutional neural networks with cutout. 2. This histogram can be manipulated to apply filters that change the color space characteristics of an image. Table5 shows that the first and second experts were only able to correctly label 62.5% and 58.6% of the GAN-generated liver lesion images as fake. Shorten C, Khoshgoftaar T. A survey on image data augmentation for deep learning. An example of the domains in this problem is neutral to disgust. Feature space augmentations can be implemented with auto-encoders if it is necessary to reconstruct the new instances back into input space. Marius C, Mohamed O, Sebastian R, Timo R, Markus E, Rodrigo B, Uwe F, Stefan R, Bernt S. The cityscape dataset for semantic urban scene understanding. Additionally, this experiment derived the same accuracy when using two Network-As in the augmentation framework as was found with one Network-A. Konno and Iwazume [74] find a performance boost on CIFAR-100 from 66 to 73% accuracy by manipulating the modularity of neural networks to isolate and refine individual layers after training. Models with poor generalizability have overfitted the training data. Swee KL, Yi L, Ngoc-Trung T, Ngai-Man C, Gemma R, Yuval E. DOPING: generative data augmentation for unsupervised anomaly detection with GAN. Accelerated magnetic resonance imaging by adversarial neural network. Philip TJ, Amir AA, Stephen B, Toby B, Boguslaw O. Deep neural networks typically rely on large amounts of training data to avoid overfitting. To further understand the effectiveness of adding GAN-generated instances, a t-SNE visualization is used. Yanghao L, Naiyan W, Jiaying L, Xiaodi H. Demistifying neural style transfer. Krizhevsky A, Sutskever I, Hinton GE. The plot on the left shows an inflection point where the validation error starts to increase as the training rate continues to decrease. Storing augmented datasets in memory is especially problematic when augmenting big data. One-shot and Zero-shot learning [16, 17] algorithms represent another paradigm for building models with extremely limited data. Curriculum learning decisions are especially important for One-Shot Learning systems such as FaceNet, presented by Schroff et al. 2018. Finally, in many of the application domains covered such as medical image analysis, the biases distancing the training data from the testing data are more complex than positional and translational variances. With adversarial training, the error rate of adversarial examples fell from 89.4% to 17.9% (Fig. Xie et al. Random erasing works by randomly selecting an nm patch of an image and masking it with either 0s, 255s, mean pixel values, or random values. This has been fueled by the advancement of deep network architectures, powerful computation, and access to big data. The contrast between random cropping and translations is that cropping will reduce the size of the input such as (256,256)(224, 224), whereas translations preserve the spatial dimensions of the image. Han Z, Tao X, Hongsheng L, Shaoting Z, Xiaogang W, Xiaolei H, Dimitris M. StackGAN: text to photo-realistic image synthesis with stacked generative adversarial networks. Deep learning has achieved remarkable results in many computer vision tasks. Illustration of GAN concept provided by Mikolajczyk and Grochowski [72]. Additionally, random cropping can also be used to provide an effect very similar to translations. J Big Data. Very interestingly as well, the policies learned on the ImageNet dataset were successful when transferred to the Stanford Cars and FGVC Aircraft image recognition tasks. Karen S, Andrew Z. This high labor and time cost motivates the use and development of synthetic datasets. On the Feret dataset, accuracy improved from 83.52 to 88.46%. The models trained on 256256 images and 512512 images achieve 7.96% and 7.42% top-5 error rates, respectively. Hiroshi I. PDF A survey on Image Data Augmentation for Deep Learning - ResearchGate The Tiny-imagenet-200 dataset is used to simulate limited data. The lower-dimensional representations of image data in fully-connected layers can be extracted and isolated. A survey on Image Data Augmentation for Deep Learning Data Augmentation, the focus of this survey, is not the only technique that has been developed to reduce overfitting. In: European conference on computer vision (ECCV); 2016. Trying to replicate the experiments of Tobin et al. Practitioners of meta-learning will have to solve problems primarily with vanishing gradients [118], amongst others, to train these networks. Neural Networks. Additionally, the comparative study ranged across dataset sizes with the small set consisting of 2k samples with 200 in each class, tthe medium set consisting of 10k samples with 1k in each class, and the large set consisting of 50k samples with 5k in each class. Artificially inflating datasets using the methods discussed in this survey achieves the benefit of big data in the limited data domain. 2018. 2008. Practitioners have the choice between using generators which transform data on the fly during training or transforming the data beforehand and storing it in memory. In: Stanford University research report, 2017. As shown throughout Design considerations for image Data Augmentation section, possibilities for Data Augmentation. Christopher B, Liang C, Ricardo GPB, Roger G, Alexander H, David AD, Maria VH, Joanna W, Daniel R. GAN augmentation: augmenting training data using generative adversarial networks. The authors highlight anecdotes of the manual annotation costs required to build these pixel-level datasets. 2018. [39]. arXiv preprint. This is a rare example of adding noise to the loss layer, whereas most of the other augmentation methods discussed add noise into the input or hidden representation layers. Overfitting is generally not as much of an issue with access to big data. This is very effective since many image datasets share low-level spatial characteristics that are better learned with big data. In this paper, a survey of data augmentation for digital images in deep learning will be presented. This has produced very amazing results on facial images. Zero-shot learning with semantic output codes. On the gender recognition task, the image to the left is an example of an instance produced by Network-A in Smart Augmentation given the right images as input [37]. Jaderberg et al. In this study, we perform a comprehensive survey of image augmentation for deep learning using a novel informative taxonomy. IEEE Trans Syst. 2017. CoRR, abs/1501.02876, 2015. A large-scale study. Some classification models lie on the fence in terms of their necessity for speed. Leon AG, Alexander SE, Matthias B. [25] in 2017. Their experiments randomize the position and texture of the objects to be detected on the table in the simulation, as well as the texture, lighting, number of lights, and random noise in the background. Joffrey LL, Taghi MK, Richard AB, Naeem S. A survey on addressing high-class imbalance in big data. Learning: an introduction. Kang et al. An extension of this will be to parameterize the geometries of random erased patches and learn an optimal erasing configuration. Neural networks are incredibly powerful at mapping high-dimensional inputs into lower-dimensional representations. arXiv preprint. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The interesting ways to augment image data fall into two general categories: data warping and oversampling. Ross G. Fast R-CNN. Jonathan K, Michael S, Jia D, Li F-F. 3D object representations for fine-grained categorization. This section will briefly describe some additional design decisions with respect to Data Augmentation techniques on image data. The current image data augmentation techniques which can be used to increase the number of images are delivered and discussed and the state of the art of using image data Augmentation techniques in the different deep learning research and application is surveyed. These methods use a prepended neural network to learn Data Augmentations via mixing images, Neural Style Transfer, and geometric transformations. Using Reinforcement Learning algorithms such as NAS on the generator and discriminator architectures seem very promising. Consequently, a number of works employ multiple deep neural network branches within an end-to-end deep learning framework for image transformation in the context of data augmentation [74], [75], [76]. 2018. The following few paragraphs will introduce other solutions available to avoid overfitting in Deep Learning models. Image Data Augmentation techniques discusses each image augmentation technique in detail along with experimental results. Salimans et al. 2016. In: AAAI. Hessam B, Maxwell H, Mohammad R, Ali F. Label refinery: improving imagenet classification through label progression. These samples can be fed into further GANs and dramatically increase the size of the original dataset. [106]. Data Augmentation prevents overfitting by modifying limited datasets to possess the characteristics of big data. Data Augmentation methods such as GANs and Neural Style Transfer can imagine alterations to images such that they have a better understanding of them. Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. This is surprising because it completely defies intuition about how these models represent images. A neural algorithm of artistic style. These experiments were done using the ResNet [3] CNN architecture (Figs. In: Proceedings of OSDI. Another drawback of GANs is that they require a substantial amount of data to train. [78] show that 70.97% of images can be misclassified by changing just one pixel. We are interested in seeing how the time-series component in video data impacts the use of static image augmentation techniques. The AlexNet CNN architecture developed by Krizhevsky et al. This is able to produce acceptable images on a simple image dataset such as the MNIST handwritten digits. The worst out of the random transformations reduced the accuracy of MNIST by 26%, CIFAR10 by 72% and ImageNet (Top 1) by 28%. If color space transforms repeatedly change the color space such that the model cannot recognize red blood from green paint, the model will perform poorly on Image Sentiment Analysis. All experiments are tested with a convolutional network consisting of 3 convolutional layers each followed by max pooling and batch normalization, followed by 2 fully-connected layers. Xinyue Z, Yifan L, Zengchang Q, Jiahong L. Emotion classification with data augmentation using generative adversarial networks. Many augmentations have been proposed which can generally be classified as either a data warping or oversampling technique. Their test-time augmentation method uses a Monte Carlo simulation in order to obtain parameters for different augmentations such as flipping, scaling, rotation, and translations, as well as noise injections. Brostow Gabriel J, Fauqueur Julien, Cipolla Roberto. CNN visualization has been led by Yosinski et al. A quick taxonomy of the Data Augmentations is depicted below in Fig. A survey on Image Data Augmentation for Deep Learning A Comprehensive Survey of Image Augmentation Techniques for Deep Learning [119] which compared GANs, WGANs, flipping, cropping, shifting, PCA jittering, color jittering, adding noise, rotation, and some combinations on the CIFAR-10 and ImageNet datasets. In: IEEE access. [127]. They looked at using non-linear methods to combine images into new training instances. [124] train exclusively with synthetic data for natural scene text recognition. Image Data Augmentation for Deep Learning: A Survey Random erasing data augmentation. For example, something like PatchShuffle Regularization could be implemented with a convolution layer. These obstacles have led to many studies on image Data Augmentation, especially GAN-based oversampling, from the application perspective of medical image classification. 2 Classical image data augmentation. Chen S, Abhinav S, Saurabh S, Abhinav G. Revisting unreasonable effectivness of data in deep learning era. Geng et al. A survey on Image Data Augmentation for Deep Learning - cjf8899.github.io. Zhang et al. A disadvantage to random erasing is that it will not always be a label-preserving transformation. ECCV. Dwarikanath M, Behzad B. Retinal vasculature segmentation using local saliency maps and generative adversarial networks for image super resolution. P. 7348. 2010;11:62560. Design considerations for image Data Augmentation discusses additional characteristics of augmentation such as test-time augmentation and the impact of image resolution. Tim S, Ian G, Wojciech Z, Vicki C, Alec R, Xi C. Improved techniques for training GANs. This section will discuss using adversarial training as a search algorithm as well as the phenomenon of adversarial attacking. The hyperparameter settings that achieved this consisted of 22 filters and a 0.05 probability of swapping. This can be seen as analogous to ensemble learning techniques in the data space. arXiv preprint. In: ISIC skin image analysis workshop and challenge @ MICCAI 2018. This differs from Transfer Learning because in Transfer Learning, the network architecture such as VGG-16 [2] or ResNet [3] must be transferred as well as the weights. t-SNE [87] is a visualization technique that learns to map between high-dimensional vectors into a low-dimensional space to facilitate the visualization of decision boundaries (Fig. A non-label preserving transformation could potentially strengthen the models ability to output a response indicating that it is not confident about its prediction. Karl W, Taghi MK, DingDing W. A survey of transfer learning. CycleGAN introduces an additional Cycle-Consistency loss function to help stabilize GAN training. AutoAugment also achieved an 83.54% Top-1 accuracy on the ImageNet dataset. 2018. Neural Style Transfer is a much more powerful augmentation technique than traditional color space augmentations, but even these methods can be combined together. In addition to improving the resolution size of GANs, another interesting architecture that increases the quality of outputs is the CycleGAN [92] proposed by Zhu et al. Understanding the relationship between transferred data domains is an ongoing research task [13]. They find classification accuracy differences of 70.18% versus 74.42% on the CIFAR-10 dataset and 74.61% versus 80.35% on the problem of classifying dogs versus cats. 2014. However, these networks are heavily reliant on big data to avoid overfitting. This is done by having two networks, Network-A and Network-B. 2014. 2014. The augmented data will represent a more comprehensive set of possible data points, thus minimizing the distance between the training and validation set, as well as any future testing sets. This is done by cutting off the output layer of the network, such that the output is a low-dimensional vector rather than a class label. Barret Z, Quoc VL. In addition to our focus on limited datasets, we will also consider the problem of class imbalance and how Data Augmentation can be a useful oversampling solution. In this study, the performance of the baseline model decreases from 74.61 to 66.87% when evaluated on augmented test images. Image Data Augmentation for Deep Learning: A Survey 2018. Additionally, meta-learning schemes can be difficult and time-consuming to implement. deep convolutional generative adversarial network, super-resolution convolutional neural network, super-resolution generative adversarial network, synthetic minority oversampling technique, Modified National Institute of Standards and Technology, t-distributed stochastic neighbor embedding. The class of augmentations discussed below could be characterized by their ease of implementation. Wu et al. Opinions, findings, conclusions, or recommendations in this paper are solely of the authors and do not reflect the views of the NSF. arXiv preprint. [14] find that transferability is negatively affected primarily by the specialization of higher layer neurons and difficulties with splitting co-adapted neurons. Adam S, Sergey B, Matthew B, Dean W, Timothy L. One-shot learning with memory-augmented neural networks. Med Image Anal. Concept behind Neural Architecture Search [33]. Illustration of the Fast neural style algorithm by Johnson et al. This style transfer is carried out via the CycleGAN [92] extension of the GAN [31] framework. The principles of adversarial training discussed above have led to the very interesting and massively popular generative modeling framework known as GANs. Li et al. [121] sought out to develop a mathematical framework to formulate test-time augmentation. Springer J Big Data. Yongqin X, Christoph HL, Bernt S, Zeynep A. Zero-shot learninga comprehensive evaluation of the good, the bad and the ugly. Data augmentation Data cleaning Data oversampling Data pre-processing Data wraping 1. [29]. A survey on Image Data Augmentation for Deep Learning Yaniv T, Ming Y, Marc AR, Lior W. DeepFace: closing the gap to human-level performance in face verification. 2016. In addition to serving as an evaluation metric, defense to adversarial attacks, adversarial training can be an effective method for searching for augmentations. In: 2018 IEEE 15th International Symposium on biomedical imaging (ISBI 2018). International Conference on Learning Representations, 2015. 2012;25:110614. These networks have the ability to generate new training data that results in better performing classification models. These weights are then fine-tuned only with the original training data. Bowles et al. This padding preserves the spatial dimensions of the image post-augmentation. Lin Z, Shi Y, Xue Z. IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection. For example, in a dog breed classification task, if there are only bulldogs and no instances of golden retrievers, no augmentation method discussed, from SamplePairing to AutoAugment to GANs, will create a golden retriever. Therefore, some manual intervention may be necessary depending on the dataset and task. Maria JMC, Sarfaraz H, Jeremy B, Ulas B. [96] sought out to compare newly developed GAN loss functions.
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