PolyConv learns continuous distributions as the convolutional filters to share the weights across different vertices of graphs or points of point clouds.

**Sample Redistribution** is a [data augmentation](https://paperswithcode.com/methods/category/image-data-augmentation) technique for face detection which augments training samples based on the statistics of benchmark datasets via large-scale cropping. During training data augmentation, square patches are cropped from the original images with a random size from the set $[0.3,1.0]$ of the short edge of the original images. To generate more positive samples for stride 8, the random size range is enlarged from $[0.3,1.0]$ to $[0.3,2.0]$. When the crop box is beyond the original image, average RGB values fill the missing pixels.

The motivation is that for efficient [face detection](https://paperswithcode.com/task/face-detection) under a fixed VGA resolution (i.e. 640×480), most of the faces (78.93%) in [WIDER FACE](https://paperswithcode.com/dataset/wider-face-1) are smaller than 32×32 pixels, and thus they are predicted by shallow stages. To obtain more training samples for these shallow stages, Sample Redistribution (SR) is used.

Sample Redistribution

Sample and Computation Redistribution for Efficient Face Detection

PolyConv

PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation

**DCGAN**, or **Deep Convolutional GAN**, is a generative adversarial network architecture. It uses a couple of guidelines, in particular:

- Replacing any pooling layers with strided convolutions (discriminator) and fractional-strided convolutions (generator).
- Using batchnorm in both the generator and the discriminator.
- Removing fully connected hidden layers for deeper architectures.
- Using [ReLU](https://paperswithcode.com/method/relu) activation in generator for all layers except for the output, which uses tanh.
- Using LeakyReLU activation in the discriminator for all layer.

Source	PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com