**MaxUp** is an adversarial data augmentation technique for improving the generalization performance of machine learning models. The idea is to generate a set of augmented data with some random perturbations or transforms, and minimize the maximum, or worst case loss over the augmented data.  By doing so, we implicitly introduce a smoothness or robustness regularization against the random perturbations, and hence improve the generation performance.  For example, in the case of Gaussian perturbation, MaxUp is asymptotically equivalent to using the gradient norm of the loss as a penalty to encourage smoothness.

**3D Dynamic Scene Graph**, or **DSG**, is a representation that captures metric and semantic aspects of a dynamic environment. A DSG is a layered graph where nodes represent spatial concepts at different levels of abstraction, and edges represent spatio-temporal relations among nodes.

3D Dynamic Scene Graph

Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs

MaxUp

MaxUp: A Simple Way to Improve Generalization of Neural Network Training

**Pointer Networks** tackle problems where input and output data are sequential data, but can't be solved by seq2seq type models because discrete categories of output elements depend on the variable input size (and are not decided in advance).

A Pointer Network learns the conditional  probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence. They solve the problem of variable size output dictionaries using [additive attention](https://paperswithcode.com/method/additive-attention). But instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, Pointer Networks use attention as a pointer to select a member of the input sequence as the output. 

Pointer-Nets can be used to learn approximate solutions to challenging geometric problems such as finding planar convex hulls, computing Delaunay triangulations, and the planar Travelling Salesman Problem.

Source	MaxUp: A Simple Way to Improve Generalization of Neural Network Training
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com