A **FuseFormer block** is used in the [FuseFormer](https://paperswithcode.com/method/fuseformer) model for video inpainting. It is the same to standard [Transformer](https://paperswithcode.com/method/transformer) block except that feed forward network is replaced with a Fusion Feed Forward Network (F3N). F3N brings no extra parameter into the standard feed forward net and the difference is that F3N inserts a soft-split and a soft composite operation between the two layer of MLPs.

**NPID++** (Non-Parametric Instance Discrimination) is a self-supervision approach that takes a non-parametric classification approach. It approves upon [NPID](https://paperswithcode.com/method/npid) by using more negative samples and training for more epochs.

NPID++

Self-Supervised Learning of Pretext-Invariant Representations

FuseFormer Block

FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting

A **Variational Autoencoder** is a type of likelihood-based generative model. It consists of an encoder, that takes in data $x$ as input and transforms this into a latent representation $z$,  and a decoder, that takes a latent representation $z$ and returns a reconstruction $\hat{x}$. Inference is performed via variational inference to approximate the posterior of the model.

Source	FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com