Image Compression | AutoEncoder

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#python #tensorflow #keras #machinelearning #artificialintelligence
Use case: Image compression
Autoencoders can be used for image compression by training them to reconstruct the input image from a lower-dimensional representation, called the bottleneck or latent representation. This compression can be useful for reducing the storage required for images or speeding up image transfer.
Here is a brief explanation of what is happening in the example code:
The first step is to load the Fashion MNIST dataset.
The second step is to preprocess the data by normalizing it to the range [0, 1].
The third step is to define the autoencoder model. The autoencoder consists of two parts: an encoder and a decoder. The encoder takes the input image as input and outputs a latent representation of the image. The decoder takes the latent representation as input and outputs the reconstructed image.
The fourth step is to train the autoencoder. We train the autoencoder by minimizing the mean squared error between the input image and the reconstructed image.
The fifth step is to encode and decode images from the test set. We encode the images from the test set using the encoder and then decode them using the decoder.
The sixth step is to plot the results. We plot the original images and the reconstructed images side by side.
I hope this helps!
Arjun Yonjan
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