Cheney et al. study the robustness of deep neural networks, especially AlexNet, with regard to randomly dropping or perturbing weights. In particular, the authors consider three types of perturbations: synapse knockouts set random weights to zero, node knockouts set all weights corresponding to a set of neurons to zero, and weight perturbations add random Gaussian noise to the weights of a specific layer. These perturbations are studied on AlexNet, considering the top-5 accuracy on ImageNet; perturbations are considered per layer. For example, Figure 1 (left) shows the influence on accuracy when knocking out synapses. As can be seen, the lower layers, especially the first convolutional layer, are impacted significantly by these perturbations. Similar observations, Figure 1 (right) are made for random perturbations of weights; although the impact is less significant. Especially high-level features, i.e., the corresponding layers, seem to be robust to these kind of perturbations. The authors also provide evidence that these results extend to the top-1 accuracy, as well as other architectures. For VGG, however, the impact is significantly less pronounced which may also be due to the employed dropout layers.

https://i.imgur.com/78T6Gg2.png
Figure 1: Left: Influence of setting weights in the corresponding layers to zero. Right: Influence of randomly perturbing weights of specific layers. Experiments are on ImageNet using AlexNet.

Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).

Cheney et al. study the robustness of deep neural networks, especially AlexNet, with regard to randomly dropping or perturbing weights. In particular, the authors consider three types of perturbations: synapse knockouts set random weights to zero, node knockouts set all weights corresponding to a set of neurons to zero, and weight perturbations add random Gaussian noise to the weights of a specific layer. These perturbations are studied on AlexNet, considering the top-5 accuracy on ImageNet; perturbations are considered per layer. For example, Figure 1 (left) shows the influence on accuracy when knocking out synapses. As can be seen, the lower layers, especially the first convolutional layer, are impacted significantly by these perturbations. Similar observations, Figure 1 (right) are made for random perturbations of weights; although the impact is less significant. Especially high-level features, i.e., the corresponding layers, seem to be robust to these kind of perturbations. The authors also provide evidence that these results extend to the top-1 accuracy, as well as other architectures. For VGG, however, the impact is significantly less pronounced which may also be due to the employed dropout layers.

Figure 1: Left: Influence of setting weights in the corresponding layers to zero. Right: Influence of randomly perturbing weights of specific layers. Experiments are on ImageNet using AlexNet.

Also find this summary at davidstutz.de.