using unsupervised learning algorithms on images I am working on a project to classify images of types of cloth (shirt, tshirt, pant etc). While this is a standard supervised classification problem, the accuracy of the neural network is not good. This is because of the close similarity of the types of cloth that I am trying to classify. 

I am working with 9 classes with around 10,000 images per class. For the classification problem I tried using CNN to classify the images. But over fitting took place with a good training accuracy (around 95%), but not so great validation accuracy (around 77%). 

I wanted to know if there was any way I could create clusters based on the type of cloth using some unsupervised learning algorithm like K Means or DBScan.
Have you included dropout in your model? It can help avoid overfitting issue. 

For your problem, yes, you can use auto-encoders, GAN, etc. for feature learning.
However, I'm not sure if unsupervised learning can help, since it's more like a training issue. Your have label with your data so supervised learning is ideal, plus supervised learning generally shows better performance than unsupervised in image classification. You might want to check the false classification examples in your dataset, and try to alter the CNN structure based on that, which would be a more direct way.