AlexNet MCQ
The 2012 ImageNet breakthrough: depth, ReLU, dropout, and training a large CNN on GPUs.
ImageNet
ILSVRC 2012
ReLU
Non-linearity
Dropout
Regularization
GPUs
Scale
AlexNet in context
AlexNet (Krizhevsky et al., 2012) won ImageNet ILSVRC with a large GPU-trained CNN. It popularized ReLU activations, dropout regularization, overlapping max pooling, data augmentation, and multi-GPU model parallelism for vision. Deeper stacks of conv layers followed (VGG, ResNet, …).
Why it mattered
It showed that deep CNNs scaled with data and compute could dominate hand-crafted features on a hard benchmark.
Key ideas
Architecture
Five conv layers (with LRN and pool stages) then three FC layers.
ReLU
Faster training than saturating sigmoids/tanh; helps deep nets converge.
Dropout
Randomly drops activations in FC layers to reduce co-adaptation / overfitting.
Scale
Trained on two GPUs with split conv layers—enabled larger width.
Rough data flow
227×227 input → conv/pool stages → 4096-4096-1000 FC → softmax