Illustrated FixMatch for semi-supervised learning
I don't know much about ML/Deep-Learning and I have a burning question:Say we have 10 images as a starting point. Then we create 10,000 images from those 10 images by adding noise, filters, flip them, skew them, distort them, etc. Isn't the underlying data the same (or some formal definition of shannon information entropy)? Would that actually improve neural networks?I've always wondered. Is it possible to generate infinite data and get almost perfect neural network accuracy?