Upsampling methods

Upsampling is a procedure where synthetically generated data points (corresponding to minority class) are injected into the dataset. After this process, the counts of both labels are almost the same. This equalization procedure prevents the model from inclining towards the majority class.

1. What is upsampling in deep learning?
2. How do you upsample data?
3. What is upsampling used for?
4. What is upsampling in image processing?

What is upsampling in deep learning?

The Upsampling layer is a simple layer with no weights that will double the dimensions of input and can be used in a generative model when followed by a traditional convolutional layer.

How do you upsample data?

You can upsample a dataset by simply copying records from minority classes. You can do so via the resample() method from the sklearn. utils module, as shown in the following script. You can see that in this case, the first argument we pass the resample() method is our minority class, i.e. our spam dataset.

What is upsampling used for?

The purpose of Upsampling is to manipulate a signal in order to artificially increase the sampling rate.

What is upsampling in image processing?

Upsampling is the increasing of the spatial resolution while keeping the 2D representation of an image. It is typically used for zooming in on a small region of an image, and for eliminating the pixelation effect that arises when a low-resolution image is displayed on a relatively large frame.