Skip to content

ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)

Authors: Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton Year: 2012 Citations: 80,000+

Summary

Deep 8-layer CNN winning ImageNet 2012 with dramatic accuracy jump. Introduces ReLU, dropout, GPU training, data augmentation. Triggered deep learning revolution.

Key Concepts

  • Deep Architecture: 8 convolutional layers much deeper than prior work
  • ReLU Activation: Non-saturation enabling faster training
  • Dropout Regularization: Random unit dropping prevents overfitting
  • GPU Training: Accelerated computation on commodity hardware
  • Data Augmentation: Random crops and flips increase diversity
  • End-to-End Learning: Backpropagation through all layers

Impact

  • 80,000+ citations
  • Triggered deep learning revolution
  • Showed deep networks work for vision
  • Introduced key techniques (ReLU, dropout)
  • Drove GPU adoption
  • Inspired ResNet, VGGNet, Inception
  • Transformed vision from features to learned representations
  • LeNet (LeCun et al., 1998)
  • VGGNet (Simonyan & Zisserman, 2014)
  • ResNet (He et al., 2015)