A **Harmonic Network**, or **Harm-Net**, is a type of convolutional neural network that replaces convolutional layers with "harmonic blocks" that use [Discrete Cosine Transform](https://paperswithcode.com/method/discrete-cosine-transform) (DCT) filters. These blocks can be useful in  truncating high-frequency information (possible due to the redundancies in the spectral domain).

**VoVNetV2** is a convolutional neural network that improves upon [VoVNet](https://paperswithcode.com/method/vovnet) with two effective strategies: (1) [residual connection](https://paperswithcode.com/method/residual-connection) for alleviating the optimization problem of larger VoVNets and (2) effective Squeeze-Excitation (eSE) dealing with the channel information loss problem of the original squeeze-and-excitation module.

VoVNetV2

CenterMask : Real-Time Anchor-Free Instance Segmentation

Harm-Net

Harmonic Convolutional Networks based on Discrete Cosine Transform

A **BiGAN**, or **Bidirectional GAN**, is a type of generative adversarial network where the generator  not only maps latent samples to generated data, but also has an inverse mapping from data to the latent representation. The motivation is to make a type of GAN that can learn rich representations for us in applications like unsupervised learning.

In addition to the generator $G$ from the standard [GAN](https://paperswithcode.com/method/gan) framework, BiGAN includes an encoder $E$ which maps data $\mathbf{x}$ to latent representations $\mathbf{z}$. The BiGAN discriminator $D$ discriminates not only in data space ($\mathbf{x}$ versus $G\left(\mathbf{z}\right)$), but jointly in data and latent space (tuples $\left(\mathbf{x}, E\left(\mathbf{x}\right)\right)$ versus $\left(G\left(z\right), z\right)$), where the latent component is either an encoder output $E\left(\mathbf{x}\right)$ or a generator input $\mathbf{z}$.

Source	Harmonic Convolutional Networks based on Discrete Cosine Transform
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com