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))

Recent deep neural network models trained on smaller and less diverse datasets use data augmentation to alleviate limitations such as overfitting, reduced robustness, and lower generalization. Methods using 3D datasets are among the most common to use data augmentation techniques such as random point drop, scaling, translation, rotations, and jittering. However, these data augmentation techniques are fixed and are often applied to the entire object, ignoring the object’s local geometry. Different local neighborhoods on the object surface hold a different amount of geometric complexity. Applying the same data augmentation techniques at the object level is less effective in augmenting local neighborhoods with complex structures. This paper presents PatchAugment, a data augmentation framework to apply different augmentation techniques to the local neighborhoods. Our experimental studies on PointNet++ and DGCNN models demonstrate the effectiveness of PatchAugment on the task of 3D Point Cloud Classification. We evaluated our technique against these models using four benchmark datasets, ModelNet40 (synthetic), ModelNet10 (synthetic), SHREC’16 (synthetic), and ScanObjectNN (real-world).

[[ICCVW 2021]](https://openaccess.thecvf.com/content/ICCV2021W/DLGC/papers/Sheshappanavar_PatchAugment_Local_Neighborhood_Augmentation_in_Point_Cloud_Classification_ICCVW_2021_paper.pdf) PatchAugment: Local Neighborhood Augmentation in Point Cloud Classification. [[Code]](https://github.com/VimsLab/PatchAugment)

PatchAugment

Generative Adversarial Networks

**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.

Source	Generative Adversarial Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com