Enhancements of Neural Networks and Adversarial Networks

Neural networks like recurrent nets enhance learning by feeding previous learnings into the current state, mimicking how the brain works by keeping track of history. Adversarial networks can defeat categorization by introducing noise in data, causing neural networks to fail even though humans can still recognize images. Research in introducing noise and understanding the failure of categorization is an active area of study.