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Research Topic Ideas for Data Augmentation

Research Topic Ideas for Data Augmentation

   Data availability for training a model is limited in many real-world applications, and such issues are overcome by data augmentation, which replicates the data to make the proper decision. Data augmentation is a data analytics strategy that increases the amount of data by creating new synthetic data or adding slightly modified copies from existing data. The significant benefits of data augmentation are improved model precision, accuracy, cost reduction in collecting and labeling the data. In machine learning models, data augmentation act as a regularizer and overfitting reduction. Image data augmentation produces an enhanced version of the image and uses geometric and color space transformations such as Flipping, Rotation, Translation, Cropping, Scaling, color casting, Varying brightness, and noise injection. Deep learning-based data augmentation improves the data availability by artificially creating new training data from the existing one, and the deep learning model used in such cases are generative adversarial networks.
    Data augmentation techniques in NLP applications are Rule-Based Techniques, for Example, Interpolation Techniques and Model-Based Techniques. Data augmentation is widely used in computer vision and natural language processing(NLP). In computer vision, data augmentation is applied in tasks such as image classification, facial recognition, object detection, and segmentation. Question answering, fixing class imbalance, low resource languages, summarization, neural machine translation, parsing tasks, sequence tagging tasks, few-shot learning, and many more. Other application areas of data augmentation are signal processing and speech recognition. The advanced developments of data augmentation are Neural Style Transfer, adversarial training, GANs, meta-learning APIs, and learning augmentation for deep reinforced learning and future directions of data augmentation in NLP are More Exploration on Pretrained Language Models, More Generalized Methods for NLP, Working with Long Texts and Low Resources Languages.