Data generator tensorflow series#
Changing the speed: stretches times series by a fixed rate.Shifting: shift audio left (fast forward) or right with random seconds.Noise injection: add gaussian or random noise to the audio dataset to improve the model performance.In this section, we will learn about audio, text, image, and advanced data augmentation techniques. Finding an effective data augmentation approach can be challenging.For example, generating high-resolution images using GANs can be challenging. Research and development are required to build a system with advanced applications.Quality assurance for data augmentation is expensive.The biases in the original dataset persist in the augmented data.To Reduce the operational cost of labeling and cleaning the raw dataset.
You can augment audio, video, text, and other types of data too. Note: the augmentation techniques are not limited to images. It often uses DNNs (Deep Neural Networks) and GANs (Generative Adversarial Networks) to generate synthetic data. Synthetic data is generated artificially without using the original dataset. In the case of image augmentation, we make geometric and color space transformations (flipping, resizing, cropping, brightness, contrast) to increase the size and diversity of the training set. Synthetic dataĪugmented data is driven from original data with some minor changes. It includes making minor changes to the dataset or using deep learning to generate new data points. Data augmentation is a technique of artificially increasing the training set by creating modified copies of a dataset using existing data.
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