Enhancing Performance of Deep Learning Models with different Data Augmentation Techniques: A Survey
By Cherry Khosla, Baljit Singh Saini. 2020
152 citations recorded by the source corpus.
Deep convolutional neural networks have shown impressive results on different computer vision tasks. Nowadays machines are fed by new artificial intelligence techniques which makes them intelligent enough to cognize the visual world better than humans. These computer vision algorithms rely heavily on large data sets. Having a large training data set plays a very crucial role in the performance of deep convolutional neural networks. We can enhance the performance of the model by augmenting the data of the image. Data augmentation is a set of techniques that are used to increase the size and quality of the image with label preserving transformations. This survey paper focuses on different data augmentation techniques based on data warping and oversampling. In addition to data augmentation techniques, this paper gives a brief discussion on different solutions of reducing the overfitting problem.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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Citations in the corpus
- On The Impact of Machine Learning Randomness on Group Fairness (2023, background): “For example, data augmentation to increase dataset size [31], dropout to perform regularization [58], gradient noise for private training [14], etc.”
- Image Data Augmentation for Deep Learning: A Survey (2022, background): “One work mainly focuses on different data augmentation techniques based on data warping and oversampling [Khosla and Saini, 2020].”