**CSPDarknet53** is a convolutional neural network and backbone for object detection that uses [DarkNet-53](https://paperswithcode.com/method/darknet-53). It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network. 

This CNN is used as the backbone for [YOLOv4](https://paperswithcode.com/method/yolov4).

**WaveGAN** is a generative adversarial network for unsupervised synthesis of raw-waveform audio (as opposed to image-like spectrograms). 

The WaveGAN architecture is based off [DCGAN](https://paperswithcode.com/method/dcgan). The DCGAN generator uses the [transposed convolution](https://paperswithcode.com/method/transposed-convolution) operation to iteratively upsample low-resolution feature maps into a high-resolution image. WaveGAN modifies this transposed [convolution](https://paperswithcode.com/method/convolution) operation to widen its receptive field, using a longer one-dimensional filters of length 25 instead of two-dimensional filters of size 5x5, and upsampling by a factor of 4 instead of 2 at each layer. The discriminator is modified in a similar way, using length-25 filters in one dimension and increasing stride
from 2 to 4. These changes result in WaveGAN having the same number of parameters, numerical
operations, and output dimensionality as DCGAN. An additional layer is added afterwards to allow for more audio samples. Further changes include:

1. Flattening 2D convolutions into 1D (e.g. 5x5 2D conv becomes length-25 1D).
2. Increasing the stride factor for all convolutions (e.g. stride 2x2 becomes stride 4).
3. Removing [batch normalization](https://paperswithcode.com/method/batch-normalization) from the generator and discriminator.
4. Training using the [WGAN](https://paperswithcode.com/method/wgan)-GP strategy.

WaveGAN

Adversarial Audio Synthesis

CSPDarknet53

YOLOv4: Optimal Speed and Accuracy of Object Detection

A **Multiscale Dilated Convolution Block** is an Inception-style convolutional block motivated by the ideas that image features naturally occur at multiple scales, that a network’s expressivity is proportional to the range of functions it can represent divided by its total number of parameters, and by the desire to efficiently expand a network’s receptive field. The Multiscale [Dilated Convolution](https://paperswithcode.com/method/dilated-convolution) (MDC) block applies a single $F\times{F}$ filter at multiple dilation factors, then performs a weighted elementwise sum of each dilated filter’s output, allowing the network to simultaneously learn a set of features and the relevant scales at which those features occur with a minimal increase in parameters. This also rapidly expands the network’s receptive field without requiring an increase in depth or the number of parameters.

Source	YOLOv4: Optimal Speed and Accuracy of Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com