To make an autoencoder which takes input of images and returns denoised images.
The images in CIFAR-10 dataset are of size 32323. Since the image size is small, fully connected encoders can be used. Convolutional autoencoders can also be used to reduce image size further and check feature extraction.
4 layers of fully connected nature with 3072 units in each layer is used. 1 Input layer, 2 hidden layers, 1 output layer.
Random numbers of normal distribution are used to introduce noise. Noise, as a function of noise level l (%) is given as:
f = ( (max(image) - (image) )*l/100 )*randn(image.shape) + image
To check noise reduction, following loss function is used:
loss = log(sum( abs(X-Y)**torch.abs(X-Y) )) *100/3072
Following is a plot of loss function against noise level l:
