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The 2012 paper “ImageNet Classification with Deep Convolutional Neural Networks” by Krizhevsky, Sutskever, and Hinton introduced AlexNet, a deep CNN that dramatically improved image classification accuracy on ImageNet, halving the top-5 error rate from \~26% to \~15%. Its innovations — like ReLU activations, dropout, GPU training, and data augmentation — sparked the deep learning revolution, laying the foundation for modern computer vision and advancing AI across industries.

The 2014 paper “Generative Adversarial Nets” (GAN) by Ian Goodfellow et al. introduced a groundbreaking framework where two neural networks — a generator and a discriminator — compete in a minimax game: the generator tries to produce realistic data, while the discriminator tries to distinguish real from fake. This approach avoids Markov chains and approximate inference, relying solely on backpropagation. GANs revolutionized generative modeling, enabling realistic image, text, and audio generation, sparking massive advances in AI creativity, deepfake technology, and research on adversarial training and robustness.