Deep Residual Learning for Image Recognition (ResNet)

Before ResNet, adding more layers to a deep neural network usually helped performance up to a point — and then made things worse, even on the training data itself, which ruled out overfitting as the explanation. The real problem was that training signals (gradients) had to travel backward through every layer during training, and in very deep networks that signal would shrink to almost nothing by the time it reached the earliest layers (the 'vanishing gradient' problem), making those layers nearly impossible to train properly. He, Zhang, Ren, and Sun's fix: add 'skip connections' that let the signal bypass a block of layers entirely if needed, so a layer only has to learn the difference (the 'residual') from what came before, rather than reconstructing the whole transformation from scratch.