a survey on image data augmentation for deep learning

a survey on image data augmentation for deep learning

Random erasing data augmentation. The safety of a Data Augmentation method refers to its likelihood of preserving the label post-transformation. Analogous to learning music, a model that can classify ImageNet images will likely perform better on CIFAR-10 images than a model with random weights. However, CycleGAN learns to translate from a domain of images to another domain, such as horses to zebras. Data augmentation Data cleaning Data oversampling Data pre-processing Data wraping 1. (PDF) Unsupervised Domain Adaptation Learning with Deep - ResearchGate The concept of mixing images in an unintuitive way was further investigated by Summers and Dinneen [66]. Data Augmentation for Deep Graph Learning: A Survey The deep learning-based image segmentation approach has evolved into the mainstream of target detection and shape characterization in . David GL. In: CVPR, 2016. arXiv preprint. Quality upsampling on CIFAR-10 images from even 32323 to 64643 could lead to better and more robust image classifiers. 3, 4). Navneet D, Bill T. Histograms of oriented gradients for human detection. The future of Data Augmentation is very bright. Conditional generative adversarial nets. Choosing which styles to sample from can be a challenging task. Perez and Wang tested their algorithm on the MNIST and Tiny-imagenet-200 datasets on binary classification tasks such as cat versus dog. 2014. The results of the experiment are very promising. Lin Z, Shi Y, Xue Z. IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection. Maayan F-A, Eyal K, Jacob G, Hayit G. GAN-based data augmentation for improved liver lesion classification. Understanding mixup training methods. Privacy Feature space augmentations can be implemented with auto-encoders if it is necessary to reconstruct the new instances back into input space. Ross G, Jeff D, Trevor D, Jitendra M. Rich feature hierarchies for accurate object detection and semantic segmentation. The adversarial attacks demonstrate that representations of images are much less robust than what might have been expected. All authors read and approved the final manuscript. Finally, Wong et al. Proc IEEE. Lower-dimensional representations found in high-level layers of a CNN are known as the feature space. 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. Inspired by the mechanisms of dropout regularization, random erasing can be seen as analogous to dropout except in the input data space rather than embedded into the network architecture. The results of their technique, as well as SamplePairing and mixup augmentation, demonstrate the sometimes unreasonable effectiveness of big data with Deep Learning models (Fig. 30). arXiv preprints. One possible explanation for this is that the increased dataset size results in more robust representations of low-level characteristics such as lines and edges. Michal Z, Konrad Z, Negar R, Pedro OP. In: International conference on learning representations (ICLR); 2017. Alexander B, Alex P, Eugene K, Vladimir II, Alexandr AK. 2002;16:32157. A comprehensive survey of recent trends in deep learning for digital [3] use the same 10-crop testing procedure to evaluate their ResNet CNN architecture (Fig. The auto-encoder learns a low-dimensional representation of these data points such that vector operations such as adding and subtracting can be used to simulate a front view-3D rotation of a new instance. Most of the augmentations surveyed operate in the input layer. The researcher found even better results when testing a reduced size dataset, reducing CIFAR-10 to 1000 total samples with 100 in each class. Li et al. Another interesting framework that could be used in an adversarial training context is to have an adversary change the labels of training data. The Neural Augmentation technique performs significantly better on the Dogs versus Goldfish study and only slightly worse on Dogs versus Cats. Xudong M, Qing L, Haoran X, Raymond YKL, Zhen W, Stephen PS. Figure 1: A taxonomy of image data augmentation methods. However, it is difficult to aggregate predictions on geometrically transformed images in object detection and semantic segmentation. Data Augmentation is not limited to the image domain and can be useful for text, bioinformatics, tabular records, and many more. Mixing images through random image cropping and patching [68]. Bowles et al. This listing is intended to give readers a broader understanding of the context of Data Augmentation. 22). As the original image is translated in a direction, the remaining space can be filled with either a constant value such as 0s or 255s, or it can be filled with random or Gaussian noise. After using classical augmentations to achieve 78.6% sensitivity and 88.4% specificity, they observed an increase to 85.7% sensitivity and 92.4% specificity once they added the DCGAN-generated samples. A brief description of these overfitting solutions is provided below. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 15). OpenReview.net. [102] find that replacing batch normalization with instance normalization results in a significant improvement for fast stylization (Fig. Another detail found in the study is that better results were obtained when mixing images from the entire training set rather than from instances exclusively belonging to the same class. Mikolajcyzk and Grochowski [72] presented an interesting idea to combine random erasing with GANs designed for image inpainting. These networks can map images to binary classes or to n1 vectors in flattened layers. However, the combination of images is derived exclusively from the learned parameters of a prepended CNN, rather than using the Neural Style Transfer algorithm. Many augmentations have been proposed which can generally be classified as either a data warping or oversampling technique. AutoAugment also achieved an 83.54% Top-1 accuracy on the ImageNet dataset. [99] provide a more complete description of the problems with training GANs. Deep neural networks typically rely on large amounts of training data to avoid overfitting. By using this website, you agree to our In: IEEE 2018 international interdisciplinary Ph.D. Workshop, 2018. The authors point out that the sub-policies learned from AutoAugment are inherently flawed because of the discrete search space. This idea is also very related to final dataset size and the considerations of transformation compute and available memory for storing augmented images. Improving on the Fast Gradient Sign Method, DeepFool, developed by Moosavi-Dezfooli et al. It is also possible to do feature space augmentation solely by isolating vector representations from a CNN. This architecture trains a series of networks with progressive resolution complexity. 2018. 2023 BioMed Central Ltd unless otherwise stated. Med Image Anal. Their original dataset contains 182 CT scans, (53 Cysts, 64 Metastases, and 65 Hemangiomas). Due to the challenge of constructing refined labels for post-augmented data, it is important to consider the safety of an augmentation. Image Data Augmentation for Deep Learning: A Survey Deep neural networks typically rely on large amounts of training data to avoid overfitting. [35]. Correspondence to Buda Mateusz, Maki Atsuto, Mazurowski Maciej A. 1). Neural Networks. Daojun L, Feng Y, Tian Z, Peter Y. For example, a prediction of an image should not be much different when that same image is rotated 20. Hui H, Wen-Yuan W, Bing-Huan M. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In these tests, the study asks two experts to distinguish between real and artificial images in medical image tasks such as skin lesion classification and liver cancer detection. This constraint forces the network to learn more robust features rather than relying on the predictive capability of a small subset of neurons in the network. This section describes different augmentations based on geometric transformations and many other image processing functions. He et al. Using Reinforcement Learning algorithms such as NAS on the generator and discriminator architectures seem very promising. [6]. Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. Frid-Adar et al. However, several forms of biases such as lighting, occlusion, scale, background, and many more are preventable or at least dramatically lessened with Data Augmentation. Horizontal axis flipping is much more common than flipping the vertical axis. Curriculum learning, a term originally coined by Bengio et al. Many of the images studied are derived from computerized tomography (CT) and magnetic resonance imaging (MRI) scans, both of which are expensive and labor-intensive to collect. Oversampling augmentations create synthetic instances and add them to the training set. 2008;9:243156. Densely connected convolutional networks. Part of In: IEEE Transactions on Medical Imaging. 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. Given big data, deep convolutional networks have been shown to be very powerful for medical image analysis tasks such as skin lesion classification as demonstrated by Esteva et al. In: Proceedings of BMVC. This article aims to expand the small data set in tumor segmentation based on the deep learning method.MethodsThis method includes three main parts: image cutting and mirroring augmentation . The use of evolutionary sampling [133] to find these subsets to input to GANs for class sampling is a promising area for future work. More on this topic will be discussed in Design Considerations of Data Augmentation. It is a generally accepted notion that bigger datasets result in better Deep Learning models [23, 24]. Mark P, Dean P, Geoffrey H, Tom MM. Lin et al. Many of these augmentations elucidate how an image classifier can be improved, while others do not. Once it is practical to produce high resolution outputs from GAN samples, these outputs will be very useful for Data Augmentation. Imbalanced datasets are harmful because they bias models towards majority class predictions. The study proposed a deep learning-based object detection model for pest management in agriculture, which involved comparing the performance of five Yolo-based models in detecting thistle caterpillars, red beetles, and citrus psylla. 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. arXiv preprint. Springer; 2015, p. 23441. This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing Data Augmentation. [76] find that when it is possible to transform images in the data-space, data-space augmentation will outperform feature space augmentation. Using CycleGANs to translate images from the other 7 classes into the minority classes was very effective in improving the performance of the CNN model on emotion recognition. Another indirect example of non-label preserving color transformations is in Image Sentiment Analysis [62]. In: CVPR; 2016. Since then, GANs were introduced in 2014 [31], Neural Style Transfer [32] in 2015, and Neural Architecture Search (NAS) [33] in 2017. Minh et al. Article 24). An extension of this will be to parameterize the geometries of random erased patches and learn an optimal erasing configuration. 2018. A survey on Image Data Augmentation for Deep Learning [106]. Adversarial attacking consists of a rival network that learns augmentations to images that result in misclassifications in its rival classification network. Xinyue Z, Yifan L, Zengchang Q, Jiahong L. Emotion classification with data augmentation using generative adversarial networks. [35] is much faster, but limits transfer to a pre-trained set of styles. By improving the quantity and diversity of training data, data augmentation has become an inevitable part of deep learning model training with image data. If the label of the image after a non-label preserving transformation is something like [0.5 0.5], the model could learn more robust confidence predictions. A complete survey of regularization methods in Deep Learning has been compiled by Kukacka et al. In: Proceedings of OSDI. Testing their test-time augmentation scheme on medical image segmentation, they found that it outperformed the single-prediction baseline and dropout-based multiple predictions. All of the augmentation methods discussed above are applied to images in the input space. The sequential processing of neural networks can be manipulated such that the intermediate representations can be separated from the network as a whole. 1998;6(02):10716. The interesting ways to augment image data fall into two general categories: data warping and oversampling. A survey on Image Data Augmentation for Deep Learning. ArXiv preprint. For applications such as self-driving cars it is fairly intuitive to think of transferring training data into a night-to-day scale, winter-to-summer, or rainy-to-sunny scale. Neural Style Transfer was sped up with the development of Perceptual Losses by Johnson et al. Zajac et al. In: Advances in neural information processing systems (NIPS); 2018. Improving the quality of GAN samples and testing their effectiveness on a wide range of datasets is another very important area for future work. 2017;42:60-88. . Transfer Learning works by training a network on a big dataset such as ImageNet [12] and then using those weights as the initial weights in a new classification task. If the style set is too small, further biases could be introduced into the dataset. This parameter encodes the distortional difference between a 45 rotation and a 30 rotation. These datasets will be constrained in size to test the effectiveness with respect to limited data problems. The work of Style Augmentation [103], avoids introducing a new form of style bias into the dataset by deriving styles at random from a distribution of 79,433 artistic images. known as Fast Style Transfer [35]. Jaderberg et al. This paper summarizes the principles and advantages of these algorithms and performs some algorithms. This Data Augmentation helped reduce overfitting when training a deep neural network. Adversarial attacks can help to illustrate weak decision boundaries better than standard classification metrics can. 2018. arXiv preprint. Another interesting discussion about Data Augmentation in images is the impact of resolution. Data Augmentation constructs massively inflated training from combinations such as flipping, translating, and randomly erasing. Josh T, Rachel F, Alex R, Jonas S, Wojciech Z, Pieter A. Domain randomization for transferring deep neural networks from simulation to the real world. This style transfer is carried out via the CycleGAN [92] extension of the GAN [31] framework. arXiv preprint. This problem limits this dataset to 2 classes. Dataset augmentation in feature space. 2010;11:62560. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Ian JG, David W-F, Mehdi M, Aaron C, Yoshua B. Maxout networks. In their experiments, they average the predictions on ten randomly cropped patches. 2016. These images are then mixed by averaging the pixel values for each of the RGB channels. After this, the generated zebras from horse images are passed through a network which translates them back into horses. Many experiments constrain themselves to a subset of the dataset to simulate limited data problems. Lucic et al. arXiv preprint. 2018. In: DLMIA/ML-CDS@MICCAI, 2017. As shown in Table4, the adversarial training in their experiment did not improve the test accuracy. https://doi.org/10.1186/s40537-019-0197-0, DOI: https://doi.org/10.1186/s40537-019-0197-0. Data Augmentation for Deep Graph Learning: A Survey Kaize Ding, Zhe Xu, Hanghang Tong, Huan Liu Graph neural networks, a powerful deep learning tool to model graph-structured data, have demonstrated remarkable performance on numerous graph learning tasks. The audience dataset responded with an improvement of 70.02% to 76.06%. In the MNIST dataset, each image is only 28281 for a total of 784 pixels. 2009, p. 418. Other simple image manipulations such as color augmentations, mixing images, kernel filters, and random erasing can also be extended to oversample data in the same manner as geometric augmentations. Some research suggests that it is best to initially train with the original data only and then finish training with the original and augmented data, although there is no clear consensus. Smart Augmentation is another approach to meta-learning augmentations. 2017. AutoAugment is a Reinforcement Learning algorithm [115] that searches for an optimal augmentation policy amongst a constrained set of geometric transformations with miscellaneous levels of distortions. Table9 shows the higher performance achieved when augmenting test images as well as training images. arXiv preprint. J Big Data 6, 60 (2019). 17, 18). This comes at a computational cost depending on the augmentations performed, and it can restrict the speed of the model. We can enhance the performance of the model by augmenting the data of the image. More advanced color augmentations come from deriving a color histogram describing the image. Kaiming H, Xiangyu Z, Shaoqing R, Jian S. Deep residual learning for image recognition. 2018. Matsunaga et al. Color space transformations can also be derived from image-editing apps. . Gao H, Zhuang L, Laurens M, Kilian QW. arXiv preprints. IEEE Intell Syst. CNN visualization has been led by Yosinski et al. The safety of rotation augmentations is heavily determined by the rotation degree parameter. 2018. Jelmer MW, Tim L, Max AV, Ivana I. Generative adversarial networks for noise reduction in low-dose CT. Data Augmentation is similar to imagination or dreaming. This has led to a sequence of progressively more complex architectures from AlexNet [1] to VGG-16 [2], ResNet [3], Inception-V3 [4], and DenseNet [5]. Int J Uncertain Fuzzin Know Based Syst. [1] revolutionized image classification by applying convolutional networks to the ImageNet dataset. Your privacy choices/Manage cookies we use in the preference centre. Adversarial misclassification example [81]. Another architecture of interest is known as Progressively Growing GANs [34]. Taylor and Nitschke [63] provide a comparative study on the effectiveness of geometric and photometric (color space) transformations. Design considerations for image Data Augmentation discusses additional characteristics of augmentation such as test-time augmentation and the impact of image resolution. Before discussing image augmentation techniques, it is useful to frame the context of the problem and consider what makes image recognition such a difficult task in the first place. Dua D, Karra TE. One work mainly focuses on different data augmentation tech-niques based on data warping and oversampling . In: CVPR 14. Data Augmentation methods such as GANs and Neural Style Transfer can imagine alterations to images such that they have a better understanding of them. Another interesting alternative to Reinforcement Learning is simple random search [112]. In: Proceedings of the 12th USENIX symposium on operating system design and implementation (OSDI 16), 2016. On the CIFAR-10 dataset, Ionue reported a reduction in error rate from 8.22 to 6.93% when using the SamplePairing Data Augmentation technique. 46. Esteban R, Sherry M, Andrew S, Saurabh S, Yutaka LS, Jie T, Quoc VL, Alexey K. Large-scale evolution of image classifiers. In: 2018 IEEE 15th International Symposium on biomedical imaging (ISBI 2018). The Neural Augmentation network uses this error to learn the optimal weighting for content and style images between different images as well as the mapping between images in the CNN (Fig. Terrance V, Graham WT. [117] measure robustness by distorting test images with a 50% probability and contrasting the accuracy on un-augmented data with the augmented data. 1, any shortage in data and it is labeled may reflect on the accuracy of any proposed model in machine learning . Image Data Augmentation for Deep Learning: A Survey These transformations encode many of the invariances discussed earlier that present challenges to image recognition tasks. Mehdi M, Simon O. Impact of test-time data augmentation for skin lesion classification [122]. A disadvantage of this technique is that it is very similar to the internal mechanisms of CNNs. Many constraints such as low-fidelity cameras cause these models to generalize poorly when trained in physics simulations and deployed in the real-world. A survey on Image Data Augmentation for Deep Learning This results in smaller images, heightwidth1, resulting in faster computation. arXiv preprint. Hiroshi I. The geometric transformations studied were flipping, 30 to 30 rotations, and cropping. The images produced by doing this will not look like a useful transformation to a human observer. CoRR, abs/1501.02876, 2015. All of the methods they used resulted in better performance compared to the baseline models (Fig. However, labeled data for real-world applications may be limited. As exciting as the potential of GANs is, it is very difficult to get high-resolution outputs from the current cutting-edge architectures. [35] in 2016. Understanding the relationship between transferred data domains is an ongoing research task [13]. This survey focuses on applications for image data, although many of these techniques and concepts can be expanded to other data domains. On the CIFAR-10 dataset, this achieved an error rate of 3.65 (Fig. In: International conference on learning representatoins, 2017. One way to discover overfitting is to plot the training and validation accuracy at each epoch during training. The Neural Augmentation approach takes in two random images from the same class. Least squares generative adversarial networks. This policy determines what actions to take at given states to achieve some goal. Using GANs to oversample data could be another effective way to increase the minority class size while preserving the extrinsic distribution. Instead of aggregating the predictions of different learning algorithms, we aggregate predictions across augmented images. Section 4 illustrates the state of the art of using image data augmen-tation techniques in deep learning while Sect. In: IEEE Access. ArXiv preprint. In: CVPR09, 2009. This includes mixing images, feature space augmentations, and generative adversarial networks (GANs). Additionally, Ulyanov et al. Introduction Machine learning applications in all technology fields and applied in real-life problems continue to diversify and increase rapidly. The authors also suggest that evolutionary algorithms or random search would be effective search algorithms as well. One such study was conducted by Shijie et al. However, test-time augmentation is a promising practice for applications such as medical image diagnosis. Examples of Color Augmentations provided by Mikolajczyk and Grochowski [72] in the domain of melanoma classification, Examples of color augmentations tested by Wu et al. Many other strategies for increasing generalization performance focus on the models architecture itself. This seems like a good solution for systems concerned with achieving very high performance scores, more so than prediction speed. With this, they achieve lower error rates on CIFAR-10, CIFAR-100, and ImageNet (Table9). Deep Learning and medical imaging became increasingly popular with the demonstration of dermatologist-level skin cancer detection by Esteva et al. arXiv preprint. Yongqin X, Christoph HL, Bernt S, Zeynep A. Zero-shot learninga comprehensive evaluation of the good, the bad and the ugly. 2017. arXiv preprint. The GAN framework possesses an intrinsic property of recursion which is very interesting. Cropping images can be used as a practical processing step for image data with mixed height and width dimensions by cropping a central patch of each image. 2016;3:9. The success of CNNs has spiked interest and optimism in applying Deep Learning to Computer Vision tasks. The use of CycleGANs was tested by Zhu et al. Cecilia S, Michael JD. The paper suggests that the likely best strategy would be to combine the traditional augmentations and the Neural Augmentations. [105] who used GANs to make their simulated data as realistic as possible (Fig. These augmentations are valuable for strengthening weak spots in the classification model. DeVries and Taylor discuss adding noise, interpolating, and extrapolating as common forms of feature space augmentation (Figs. In: ICML Deep Learning workshop; 2015. A disadvantage of feature space augmentation is that it is very difficult to interpret the vector data. In domains with very limited data, this could result in further overfitting. The development of Neural Style Transfer, adversarial training, GANs, and meta-learning APIs will help engineers utilize the performance power of advanced Data Augmentation techniques much faster and more easily. The datasets most frequently discussed are CIFAR-10, CIFAR-100, and ImageNet. 2014. In: NIPS, 2015. Using a diverse collection of GAN inpainters, the random erasing augmentation could seed very interesting extrapolations. [132] provide a systematic study specifically investigating the impact of imbalanced data in CNNs processing image data. 2017. Kernel filters are a very popular technique in image processing to sharpen and blur images. Therefore, some manual intervention may be necessary depending on the dataset and task. 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. For example, if all images are horizontally flipped and added to the dataset, the resulting dataset size changes from N to 2N. In: CVPR, 2005. With the reduced size dataset, SamplePairing resulted in an error rate reduction from 43.1 to 31.0%. In: MICCAI. In: ISIC skin image analysis workshop and challenge @ MICCAI 2018. In this study, the performance of the baseline model decreases from 74.61 to 66.87% when evaluated on augmented test images. Jiawei S, Danilo VV, Sakurai K. One pixel attack for fooling deep neural networoks. Image Data Augmentation techniques discusses each image augmentation technique in detail along with experimental results.

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