Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
The paper that started it all, it was the first deep learning paper that showed state of the art performance in a real computer vision task. It was the first deep learning based approach in the ImageNet competition, and it trounced other methods at the time. The main contribution of this paper was to incorporate previously known neural net techniques in a novel, simple, deep architecture:
- ReLU activation- faster convergence
- Model parallelism- bigger network->more parameters, slightly faster
- Local Response Normalization
- Overlapping Pooling- reduces overfitting
- Dropout- reduces overfitting
- Data Augmentation- reduces overfitting
The architecture uses 5 CNN layers and 3 fully connected layers.
Things I learned
- One of the key differences in the Alexnet architecture was that it used ReLU activation functions which were relatively uncommon at the time (2012), compared to tanh layers.
- Performance on ImageNet is commonly reported using the below two metrics:
- top 1% test set error rate- The fraction of test images for which the correct label is not the same as the highest model predicted label. Scored 37.5%
- top 5%- The fraction of test images for which the correct label is not among the five labels considered most probable by the model. Scored 17.0%
- The authors use model parallelism (as opposed to data parallelism) at the kernel level
- Here as in other NLP competitions(SQuAD), the best performance is obtained by averaging by averaging the prediction of multiple networks with different weight initializations
- Rationale behind dropout is a kind of ensembling during training, where for each input, a different architecture is sampled (but with shared weights).”This technique reduces complex co-adaptations of neurons, since a neuron cannot rely on the presence of particular other neurons. It is, therefore, forced to learn more robust features that are useful in conjunction with many different random subsets of the other neurons.”
- Dropout causes learning to take longer (approximately 1.5x)
- During test time, activations of neurons are multiplied by 0.5 which is equivalent to taking the geometric mean of the network’s predictions.
- In the data augmentation step
- How exactly does model parallelism work for CNNs?
- Generally speaking, do convolutional layers have fewer parameters than fully connected ones?
- Why is the data usually normalized? And how/why does local response normalization work? Is it similar to batch normalization?
- What is the intuition behind overlapping pooling?
- Is there a principled approach to increasing depth in modern CNN models? More complex models like GoogleNet have ridiculous number of layers with millions of parameters. What’s to say that each new tweak or improvement isn’t specific to the dataset (overfit), and that these gains wouldn’t be seen elsewhere (poor generalization)?
- The feature maps are spread across 2 GPUs (purely for memory constraint reasons). So for any conv layer half the feature maps are on one GPU and the remaining on the other GPU.
- LRN isn’t used anymore. See elsewhere for the other answers (TL;DR- quicker convergence, gradient descent bounces around less Video, Notes)
- Yes, the intuition behind convnets is to reduce the number of parameters (compared to an equivalent functionality fully connected network) by making the (reasonable) assumption that images are data that have spatial properties (local spatial-correlation) and the same features(say edges) exist across the image (no need to relearn that idea). This is a bias encoded into the architecture to make it efficient in the number of parameters learned. There’s some similarity in this to how LSTMs use the same weights across time.