Here we have 2 neural networks GENERATOR and DISCRIMINATOR
DISCRIMINATOR:
- Is a simple classifier that tries to classify the images as real from the training set or fake generated images.
- Acting as an adversary to the discriminator.
- It aims to trick the discrminator, giving it generated images that look as if they've come from the training set.
- If the generator produces an image that the discriminator thinks is fake, then it would change it behavior and try again.
- Generator would trick till the discrimnator starts thinking that its generated data comes from the real training set.( Goal is to force the discriminator to have a high error rate as possible.Note that at the same time discriminator is also training. it looks at examples of real and fake images and getting better at recognizing differences between them.its goal is to have a low error rate.)
Task is to have both discriminator and generator networks with opposing goals.We define opposing generator and discriminator loss functions.
At the end of the training discriminator should not be able to tell the difference between real and fake images
MNIST GAN presents an interesting approach to training two neural networks with opposing objectives. The Generator attempts to create images that resemble the training data, while the Discriminator learns to identify whether an image is real or generated. The iterative interaction between these networks demonstrates how models can progressively improve their ability to generate and distinguish image data.
ReplyDeleteThis concept is particularly relevant to Deep Learning Projects For Final Year, where neural networks, model training, and generative approaches can be explored through practical implementations.
The use of MNIST images also connects the discussion with Image Processing Projects For Final Year, particularly where image data is processed, represented, and used as input for machine learning models.
ReplyDeleteThe Generator and Discriminator training process provides a useful example for understanding model learning, classification, loss functions, and neural-network behaviour in Machine Learning Training.
ReplyDeleteThe article also highlights how training data and model outputs can be examined to understand patterns and performance. This analytical perspective can complement Data Analysis Course concepts when working with datasets and evaluating results.
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