Performance and accuracy level of deep learning models depend on the volume and diversity of training data which is used to feed and train neural network architectures. Therefore, challenges in collecting and labelling of training data can limit development of deep learning models. Data augmentation strategy enables deep learning models to handle with needs of data by increasing size of data and providing relevant data and pattern to deep learning algorithms.
What are advanced data augmentation techniques in deep learning?
We have looked into applications in deep learning as well as other AI approaches like reinforcement learning that tend to use deep learning structures for learning.
Adversarial training is a technique in the machine learning domain to improve model performance by training the model on difficult-to-solve prediction tasks.
In adversarial training, adversarial examples are created and injected in the training dataset. A model tries to fool the other model by providing deceptive inputs (e.g adding noise on the samples of dataset). After the adversarial attack, if the model classifies the input wrongly then deep learning models are trained again by using these adversarial examples to improve model performance.
This approach makes deep learning algorithms more robust. Data augmentation with adversarial examples enriches deep learning models by providing diverse data.
There is an adversarial example below. A noise which is hardly comprehensible for people is added on “panda” image. After this transformation, the model thinks that the image is “a gibbon”.
Adversarial training is a new subject and it may be an expensive process. There are unclear areas about it, such as its benefit in reducing overfitting. Research in this field, not only for image data but also for text and audio data, is progressing.
Generative adversarial networks (GANs) based augmentation
Generative adversarial networks have capabilities to augment data for training of convolutional neural networks (CNNs). The performance of CNN models depends on the level of sufficient training data. GANs can boost capabilities of CNNs effectively by generating new samples into training dataset compared to traditional data augmentation techniques (e.g. random rotations, zooming, cropping, etc.). GANs can be used for different image generation methods such as image blending, image-to-image translation and text-to-image synthesis.
A study was published about classification of defective photovoltaic (PV) module cells in electroluminescence (EL) images. The study claims that GAN-based augmentation can improve CNN’s classification performance. The accuracy of classification with the augmented dataset shows an improvement of up to 14% according to the study.
Meta-learning data augmentation
Meta-learning or “learning-to-learn” is a subfield of machine learning. Meta learning algorithms can learn from other machine learning algorithms. In deep learning domain, it refers to optimization of neural networks via other neural networks. Meta-learning may be used to create high level elements for training neural networks. Meta-learning algorithms may have the ability to sample classes like images, so data augmentation with meta-learning can provide an advantage for deep learning models. A study examines how data augmentation is used to increase number of images for each class and create new classes into the dataset.
Neural style transfer based augmentation
Neural style transfer is commonly used in artistic applications. The style of an image (e.g. ambiance, texture and composition) created in CNNs is changed with neural style transfer. An image’s style is mixed with content of another image. Neural style transfer can be a useful method for data augmentation to increase the amount of training dataset in deep learning domain. Neural style transfer helps data augmentation transformations to decide the style while reproducing new images. There are some challenges in neural style transfer based augmentation such as efforts for deciding the style, slow running time, high storage and memory capacity.
Reinforcement learning based augmentation
Reinforcement learning (RL) can be improved with augmented data to ensure that agents can be ready to perform in a wide variety of scenarios.
If you are ready to use data augmentation in your firm, you can rely on our prioritized lists for Deep learning software and Machine learning (ML) software. A significant portion of these software packages provide tools for data augmentation
If you need help in choosing vendors who can help you get started, let us know:
This article was drafted by former AIMultiple industry analyst Ayşegül Takımoğlu.
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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