AlexNet Wins the ImageNet Challenge

Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton's deep convolutional neural network "AlexNet" wins the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2012) with a top-5 error rate of 15.3%, obliterating the runner-up's 26.2% by an unprecedented 10.8 percentage-point margin. Trained on two NVIDIA GTX 580 GPUs for six days, the 8-layer, 60-million-parameter network demonstrated that deep neural networks trained with backpropagation on GPUs could vastly outperform decades of hand-engineered computer vision features. The result — announced at ECCV 2012 in Florence and presented as a paper at NeurIPS 2012 in Lake Tahoe — is widely regarded as the "Big Bang" of the modern deep learning era. It triggered an avalanche of GPU-accelerated neural network research, convinced industry that deep learning worked at scale, and set in motion the chain of advances through ResNets, GANs, sequence models, and ultimately the Transformer architectures that produced modern large language models. Cited over 150,000 times, it remains one of the most consequential papers in computer science history.