Diffusion models applied to latent spaces, which are normally built with (Variational) Autoencoders.

**Shifted Softplus** is an activation function ${\rm ssp}(x) = \ln( 0.5 e^{x} + 0.5 )$, which [SchNet](https://paperswithcode.com/method/schnet) employs as non-linearity throughout the network in order to obtain a smooth potential energy surface. The shifting ensures that ${\rm ssp}(0) = 0$ and improves the convergence of the network. This activation function shows similarity to ELUs, while having infinite order of continuity.

Shifted Softplus

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Latent Diffusion Model

High-Resolution Image Synthesis with Latent Diffusion Models

Perhaps the most widely used approach to synthesizing new examples is called the Synthetic Minority Oversampling Technique, or SMOTE for short. This technique was described by Nitesh Chawla, et al. in their 2002 paper named for the technique titled “SMOTE: Synthetic Minority Over-sampling Technique.”

SMOTE works by selecting examples that are close in the feature space, drawing a line between the examples in the feature space and drawing a new sample at a point along that line.

Source	High-Resolution Image Synthesis with Latent Diffusion Models
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com