Following are the applications of autoencoders :
Image Denoising : Denoising images is a skill that autoencoders excel at. A noisy image is one that has been corrupted or has a little amount of noise (that is, random variation of brightness or color information in images) in it. Image denoising is used to gain accurate information about the image's content.
Dimensionality Reduction : The input is converted into a reduced representation by the autoencoders, which is stored in the middle layer called code. This is where the information from the input has been compressed, and each node may now be treated as a variable by extracting this layer from the model. As a result, we can deduce that by removing the decoder, an autoencoder can be utilised for dimensionality reduction, with the coding layer as the output.
Feature Extraction : The encoding section of Autoencoders aids in the learning of crucial hidden features present in the input data, lowering the reconstruction error. During encoding, a new collection of original feature combinations is created.
Image Colorization : Converting a black-and-white image to a coloured one is one of the applications of autoencoders. We can also convert a colourful image to grayscale.
Data Compression : Autoencoders can be used for data compression. Yet they are rarely used for data compression because of the following reasons:
* Lossy compression : The autoencoder's output is not identical to the input, but it is a near but degraded representation. They are not the best option for lossless compression.
* Data-specific : Autoencoders can only compress data that is identical to the data on which they were trained. They differ from traditional data compression algorithms like jpeg or gzip in that they learn features relevant to the provided training data. As a result, we can't anticipate a landscape photo to be compressed by an autoencoder trained on handwritten digits.