**AdvProp** is an adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to the method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples.

**Hierarchical-Split Block** is a representational block for multi-scale feature representations. It contains many hierarchical split and concatenate connections within one single [residual block](https://paperswithcode.com/methods/category/skip-connection-blocks). 

Specifically, ordinary feature maps in deep neural networks are split into $s$ groups, each with $w$ channels. As shown in the Figure, only the first group of filters can be straightly connected to next layer. The second group of feature maps are sent to a convolution of $3 \times 3$ filters to extract features firstly, then the output feature maps are split into two sub-groups in the channel dimension. One sub-group of feature maps straightly connected to next layer, while the other sub-group is concatenated with the next group of input feature maps in the channel dimension. The concatenated feature maps are operated by a set of $3 \times 3$ convolutional filters. This process repeats several times until the rest of input feature maps are processed. Finally, features maps from all input groups are concatenated and sent to another layer of $1 \times 1$ filters to rebuild the features.

Hierarchical-Split Block

HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network

AdvProp

Adversarial Examples Improve Image Recognition

**ASLFeat** is a convolutional neural network for learning local features that uses deformable convolutional networks to densely estimate and apply local transformation. It also takes advantage of the inherent feature hierarchy to restore spatial resolution and low-level details for accurate keypoint localization. Finally, it uses a peakiness measurement to relate feature responses and derive more indicative detection scores.

Source	Adversarial Examples Improve Image Recognition
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com