arXiv preprint. With adversarial training, the error rate of adversarial examples fell from 89.4% to 17.9% (Fig. The image augmentation algorithms discussed in this survey include geometric transformations, color space augmentations, kernel filters, mixing images, random erasing, feature . For immediate reference, an ImageNet image is of resolution 2562563, totaling 196,608 pixels, a 250 increase in pixel count compared with MNIST. 21). This idea is also very related to final dataset size and the considerations of transformation compute and available memory for storing augmented images. Neural Networks. Real image data of two pests, red beetle and citrus psylla, were collected for pest management in crops. Intelligent oversampling techniques date back to SMOTE (Synthetic Minority Over-sampling Technique), which was developed by Chawla et al. How to fool radiologists with generative adversarial networks? These classes are imbalanced and the CycleGAN is used as a method of intelligent oversampling. By removing certain input patches, the model is forced to find other descriptive characteristics. This encoded representation is used for feature space augmentation. Further, they explore the robustness of classifiers with respect to test-time augmentation and find that the model trained with Reinforcement Learning augmentation search performs much better. IEEE Intell Syst. Results on the reduced CIFAR-10 dataset. These patches consist of one extracted from the center, four corner croppings, and the equivalent regions on the horizontally flipped images. Springer Nature. By improving the quantity and diversity of training data, data augmentation has become an inevitable part of deep learning model training with image data. 3, 4). Journal of Big Data, 6 (1):1-48, 2019. However, it fails to produce quality results for higher resolution, more complicated datasets. 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. Typically, just the weights in convolutional layers are copied, rather than the entire network including fully-connected layers. This over-extensive color-augmented data will cause a deep model to overfit even worse than the original. The concept of meta-learning in Deep Learning research generally refers to the concept of optimizing neural networks with neural networks. [85] describe GANs as a way to unlock additional information from a dataset. 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. 32). 2018. 2017. [49] tested the effectiveness of using DCGANs to generate liver lesion medical images. In: IEEE 2018 international interdisciplinary Ph.D. Workshop, 2018. There are many branches of study that hope to improve current benchmarks by applying deep convolutional networks to Computer Vision tasks. arXiv:2206.06544v1 [cs.CV] 14 Jun 2022 A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classication Tasks ZihanYang1*,RichardO.Sinnott1,JamesBailey1 . One of the solutions to search the space of possible augmentations is adversarial training. Joseph R, Santosh D, Ross G, Ali F. You only look once: unified, real-time object detection. Having a large training data set plays a very crucial role in the performance of deep convolutional neural networks. Geng et al. This problem limits this dataset to 2 classes. The GAN framework possesses an intrinsic property of recursion which is very interesting. [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]. A Survey of Automated Data Augmentation Algorithms for Deep Learning Deep neural networks typically rely on large amounts of training data to avoid overfitting. One indicator of a negative/highly negative image is the presence of blood. Data augmentation (Dyk and Meng 2001) is greatly important to overcome the limitation of data samples and particularly image data-sets.Data is the raw material for every machine learning algorithm, such as the means used to feed the algorithm as illustrated in Fig. The image outputted from the augmentation is then transformed with another random image via Neural Style Transfer. Adding noise to images can help CNNs learn more robust features. NIPS. Opinions, findings, conclusions, or recommendations in this paper are solely of the authors and do not reflect the views of the NSF. a original CNN model, b adding GAN-generated disgust images, c adding GAN-generated sad images, d adding both GAN-generated disgust and sad images [93]. In the MNIST dataset, each image is only 28281 for a total of 784 pixels. 2017. 2017. Christian L, Lucas T, Ferenc H, Jose C, Andrew C, Alejandro A, Andrew A, Alykhan T, Johannes T, Zehan W, Wenzhe S. Photo-realistic single image super-resolution using a generative adversarial network. Regularized evolution for image classifier architecture search. 16). They found that with enough variability in the training data style, the real-world simply appears as another variation to the model. In: ICML; 2015. In: 2017 Chinese automation congress (CAC), 2017. p. 416570. Additionally, meta-learning schemes can be difficult and time-consuming to implement. Many methods have been proposed to overcome this shortcoming with CNNs. The idea behind DCGAN is to increase the complexity of the generator network to project the input into a high dimensional tensor and then add deconvolutional layers to go from the projected tensor to an output image. Rotation augmentations are done by rotating the image right or left on an axis between 1 and 359. However, it seems intuitive that next-generation models would be trained on higher resolution images. In: AAAI. Jonathan L, Evan S, Trevor D. Fully convolutional networks for semantic segmentation. reviews existing face data augmentation works from perspec-tives of the transformation types and deep learning. Jason Y, Jeff C, Anh N, Thomas F, Hod L. Understanding neural networks through deep visualization. For example, if all the images in a dataset are centered, which is common in face recognition datasets, this would require the model to be tested on perfectly centered images as well. [89] use a generator network to learn how to fool a black-box detection system. Practitioners of meta-learning will have to solve problems primarily with vanishing gradients [118], amongst others, to train these networks. A comprehensive survey of recent trends in deep learning for digital [120] denote test-time augmentation as data distillation to describe the use of ensembled predictions to get a better representation of the image. Another very popular approach to one-shot learning is the use of memory-augmented networks [20]. A generator takes in images of horses and learns to map them to zebras such that the discriminator cannot tell if they were originally a part of the zebra set or not, as discussed above. For ease of implementation, data augmentation via Neural Style Transfer could be done by selecting a set of k styles and applying them to all images in the training set. 2016. The best patch fill method was found to be random values. The DCGAN architecture presents a strategy for using convolutional layers in the GAN framework to produce higher resolution images (Figs. Testing their test-time augmentation scheme on medical image segmentation, they found that it outperformed the single-prediction baseline and dropout-based multiple predictions. The future of Data Augmentation is very bright. The researcher found even better results when testing a reduced size dataset, reducing CIFAR-10 to 1000 total samples with 100 in each class. t-SNE visualization demonstrating the improved decision boundaries when using CycleGAN-generated samples. Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Network-A uses a series of convolutional layers to produce the augmented image. Chen S, Abhinav S, Saurabh S, Abhinav G. Revisting unreasonable effectivness of data in deep learning era. Humans imagine different scenarios based on experience. Then, the algorithms are split into three categories; model-free, model-based, and . In: International conference on computer vision (ICCV), 2017. arXiv preprints. This technique is applied to the feature space by joining the k nearest neighbors to form new instances. Adversarial training is a framework for using two or more networks with contrasting objectives encoded in their loss functions. 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. This is applied to image-to-image translation. Readers will understand how Data Augmentation can improve the performance of their models and expand limited datasets to take advantage of the capabilities of big data. Zhang et al. A survey on Image Data Augmentation for Deep Learning, https://doi.org/10.1186/s40537-019-0197-0, Design considerations for image Data Augmentation, http://www.pascal-network.org/challenges/VOC/voc2008/workshop/, http://creativecommons.org/licenses/by/4.0/. 2017;542:1158. [84] presented DisturbLabel, a regularization technique that randomly replaces labels at each iteration. In the image domain, this translates an image tensor of size heightwidthcolor channels down into a vector of size n1, identical to what was discussed with respect to feature space augmentation. Matsunaga et al. 2018. There are no existing augmentation techniques that can correct a dataset that has very poor diversity with respect to the testing data. Deep neural networks typically rely on large amounts of training data to avoid overfitting. For example, rotations and flips are generally safe on ImageNet challenges such as cat versus dog, but not safe for digit recognition tasks such as 6 versus 9. 2018. GAN samples can be used as an oversampling technique to solve problems with class imbalance. By improving the quantity and diversity of training data, data augmentation has become an inevitable part of deep learning model training with image data. However, in other application domains, the set of styles to transfer into is not so obvious. Adversarial attacking consists of a rival network that learns augmentations to images that result in misclassifications in its rival classification network. In: ISIC skin image analysis workshop and challenge @ MICCAI 2018. arXiv preprint. arXiv preprint. This decision is generally categorized as online or offline data augmentation, (with online augmentation referring to on the fly augmentations and offline augmentation referring to editing and storing data on the disk). The use of evolutionary sampling [133] to find these subsets to input to GANs for class sampling is a promising area for future work. The paper suggests that the likely best strategy would be to combine the traditional augmentations and the Neural Augmentations. Additionally, we will explore the effectiveness of test-time augmentation on object detection, comparing color space augmentations and the Neural Style Transfer algorithm. The concept of mixing images in an unintuitive way was further investigated by Summers and Dinneen [66]. arXiv preprints. It is important to also recognize an advancement of the original algorithm from Gatys et al. Due to the challenge of constructing refined labels for post-augmented data, it is important to consider the safety of an augmentation. Experimenting across different filter sizes and probabilities of shuffling the pixels at each step, they demonstrate the effectiveness of this by achieving a 5.66% error rate on CIFAR-10 compared to an error rate of 6.33% achieved without the use of PatchShuffle Regularization. The models trained on 256256 images and 512512 images achieve 7.96% and 7.42% top-5 error rates, respectively. Introduction Over the recent years, deep learning has achieved signi cant improvements in computer vision based on three key elements, e cient computing devices, powerful algorithms, and large volumes of image. 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]. Another drawback of GANs is that they require a substantial amount of data to train. Sergey I, Christan S. Batch normalization: accelerating deep network training by reducing internal covariate shift. Ren W, Shengen Y, Yi S, Qingqing D, Gang S. Deep image: scaling up image recognition. 28). AutoAugment also achieved an 83.54% Top-1 accuracy on the ImageNet dataset. Jeff D, Philipp K, Trevor D. Adversarial feature learning. 2017. Berlin: Springer; 2014. , p. 18499. Jonathan K, Michael S, Jia D, Li F-F. 3D object representations for fine-grained categorization. Using a diverse collection of GAN inpainters, the random erasing augmentation could seed very interesting extrapolations. Image Data Augmentation techniques discusses each image augmentation technique in detail along with experimental results. In: Proceedings of BMVC. However, labeled data for real-world applications may be limited. 2014;27:33208. We are interested in seeing how the time-series component in video data impacts the use of static image augmentation techniques. PDF arXiv:2206.06544v1 [cs.CV] 14 Jun 2022 - ResearchGate arXiv preprint. Utilizing evolutionary and random search algorithms is an interesting area of future work, but the meta-learning schemes reviewed in this survey are all neural-network, gradient-based. Xinyue Z, Yifan L, Zengchang Q, Jiahong L. Emotion classification with data augmentation using generative adversarial networks. Article Google Scholar Cubuk ED, Zoph B, Mane D, Vasudevan V, Le QV. Shorten C, Khoshgoftaar T. A survey on image data augmentation for deep learning. The Neural Augmentation techniques tested consist of three levels based on the design of the loss function for the augmentation net (Content loss, Style loss via gram matrix, and no loss computer at this layer). Data augmentation is a set of techniques that are used to increase the size and quality of the image with label preserving transformations. Manipulating the representation power of neural networks is being used in many interesting ways to further the advancement of augmentation techniques. The Smart Augmentation [37] approach utilizes a similar concept as the Neural Augmentation technique presented above.
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