The **MelGAN Residual Block** is a convolutional [residual block](https://paperswithcode.com/method/residual-block) used in the [MelGAN](https://paperswithcode.com/method/melgan) generative audio architecture. It employs residual connections with dilated convolutions. Dilations are used so that temporally far output activations of each subsequent layer has significant overlapping inputs. Receptive field of a stack of [dilated convolution](https://paperswithcode.com/method/dilated-convolution) layers increases exponentially with the number of layers. Incorporating these into the MelGAN generator allows us to efficiently increase the induced receptive fields of each output time-step. This effectively implies larger overlap in the induced receptive field of far apart time-steps, leading to better long range correlation.

**SimpleNet** is a convolutional neural network with 13 layers. The network employs a homogeneous design utilizing 3 × 3 kernels for convolutional layer and 2 × 2 kernels for pooling operations. The only layers which do not use 3 × 3 kernels are 11th and 12th layers, these layers, utilize 1 × 1 convolutional kernels. Feature-map down-sampling is carried out using nonoverlaping 2 × 2 max-pooling. In order to cope with the problem of vanishing gradient and also over-fitting, SimpleNet also uses batch-normalization with moving average fraction of 0.95 before any [ReLU](https://paperswithcode.com/method/relu) non-linearity.

SimpleNet

Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures

MelGAN Residual Block

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

NeRF represents a scene with learned, continuous volumetric radiance field $F_\theta$ defined over a bounded 3D volume. In a NeRF, $F_\theta$ is a multilayer perceptron (MLP) that takes as input a 3D position $x = (x, y, z)$ and unit-norm viewing direction $d = (dx, dy, dz)$, and produces as output a density $\sigma$ and color $c = (r, g, b)$. The weights of the multilayer perceptron that parameterize $F_\theta$ are optimized so as to encode the radiance field of the scene. Volume rendering is used to compute the color of a single pixel.

Source	MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com