Autoencoders for Denoising Task
An autoencoder is a neural network architecture made up of two main components: an encoder and a decoder. The encoder reduces the input data's dimensionality to a lower-dimensional representation, which the decoder then uses to reconstruct the original input data. This architecture allows the autoencoder to learn efficient representations of input data using unsupervised learning. Denoising is one of the most common applications of autoencoders, in which the network learns to remove noise from input data, such as images or signals, by reconstructing clean versions of the noisy input.
Mechanism of Encoder:
The purpose of an autoencoder is to convert input data into a lower-dimensional latent space representation. This is accomplished through a series of transformations, which typically include linear operations followed by nonlinear activation functions.
?Mechanism of Decoder
The decoder reconstructs the original input data from the encoder's lower-dimensional latent space representation. The decoder, like the encoder, performs a series of transformations that typically include linear operations followed by non-linear activation functions.
Autoencoder for denoising task:
To demonstrate the use of autoencoders, i built an autoencoder architecture involving conv layers using TensorFlow to denoise the images. The architecture is trained and tested on the MNIST Fashion Image dataset.
Below is the model architecture of the autoencoder:
Below are the results of the denoising of the images with corresponding original and noisy images:
The code for the discussed issue can be found in my GitHub repository. You can access it using the following link: