**Models Genesis**, or **Generic Autodidactic Models**, is a self-supervised approach for learning 3D image representations. The objective of Models Genesis is to learn a common image representation that is transferable and generalizable across diseases, organs, and modalities.  It consists of an encoder-decoder architecture with skip connections in between, and is trained to learn a common image representation by restoring the original sub-volume $x\_{i}$ (as ground truth) from the transformed one $\bar{x}\_{i}$ (as input), in which the reconstruction loss (MSE) is computed between the model prediction $x'\_{0}$ and ground truth $x\_{i}$. Once trained, the encoder alone can be fine-tuned for target classification tasks; while the encoder and decoder together can be fine-tuned for target segmentation tasks.

A **GAN**, or **Generative Adversarial Network**, is a generative model that simultaneously trains
two models: a generative model $G$ that captures the data distribution, and a discriminative model $D$ that estimates the
probability that a sample came from the training data rather than $G$.

The training procedure for $G$ is to maximize the probability of $D$ making
a mistake. This framework corresponds to a minimax two-player game. In the
space of arbitrary functions $G$ and $D$, a unique solution exists, with $G$
recovering the training data distribution and $D$ equal to $\frac{1}{2}$
everywhere. In the case where $G$ and $D$ are defined by multilayer perceptrons,
the entire system can be trained with backpropagation. 

(Image Source: [here](http://www.kdnuggets.com/2017/01/generative-adversarial-networks-hot-topic-machine-learning.html))

Generative Adversarial Networks

Models Genesis

**Reduction-B** is an image model block used in the [Inception-v4](https://paperswithcode.com/method/inception-v4) architecture.

Source	Models Genesis
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com