By adding distortions to the R, G, and B values of images, we still get images in which the same objects are recognizable.

Color Shifting for Data Augmentation in Image Recognition Through Deep Learning

For example, instead of taking more cat pictures, you can use one or a combination of the following methods:
- Mirroring (Horizontally/vertically flipping)
- Random cropping (Zooming in)
- Rotating
- Shearing
- Local wrapping
- Color shifting

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Getting more data may be costly and an ineffective way to prevent overfitting. However, you can augment your data to create more. We are adding synthetic data modified from our original data set. You essentially transform your data in a way where it is different form the original, but the data still fits into your given class. It is much more efficient and cost effective. 

Data Augmentation in Deep Learning

Methods of Data Augmentation in Image Recognition Through Deep Learning

- To improve the robustness of the neural network in speech recognition, noise injection is a method we can apply by adding random noise in the input to the neural network. One of the examples is to inject Gaussian noise into the input. 
- Applying noise injection to the hidden units can be highly effective when we tune the magnitude of the noise carefully. 

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