As neural networks became deeper in the early 2010s, researchers encountered a surprising obstacle. Intuitively, adding more layers should allow a model to learn more complex representations and achieve higher accuracy. However, experiments showed that beyond a certain depth, neural networks often became harder to train and sometimes even performed worse than shallower models. This…