**FLAVR** is an architecture for video frame interpolation. It uses 3D space-time convolutions to enable end-to-end learning and inference for video frame interpolation. Overall, it consists of a [U-Net](https://paperswithcode.com/method/u-net) style architecture with 3D space-time convolutions and
deconvolutions (yellow blocks). Channel gating is used after all (de-)[convolution](https://paperswithcode.com/method/convolution) layers (blue blocks). The final prediction layer (the purple block) is implemented as a convolution layer to project the 3D feature maps into $(k−1)$ frame predictions. This design allows FLAVR to predict multiple frames in one inference forward pass.

**PAUSE**, or **Positive and Annealed Unlabeled Sentence Embedding**, is an approach for learning sentence embeddings from a partially labeled dataset. It is based on a dual encoder schema that is widely adopted in supervised sentence embedding training. Each individual sample $\mathbf{x}$ contains a pair of hypothesis and premise sentences $(x\_{i},x^{\prime}_{i})$, each of which is fed into a pretrained encoder (e.g. [BERT](https://paperswithcode.com/method/bert)). As shown in Figure, the two encoders are identical during the training by sharing their weights.

PAUSE

PAUSE: Positive and Annealed Unlabeled Sentence Embedding

FLAVR

FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation

**BigBiGAN** is a type of [BiGAN](https://paperswithcode.com/method/bigan) with a [BigGAN](https://paperswithcode.com/method/biggan) image generator. The authors initially used [ResNet](https://paperswithcode.com/method/resnet) as a baseline for the encoder $\mathcal{E}$ followed by a 4-layer MLP with skip connections, but they experimented with RevNets and found they outperformed with increased network width, so opted for this type of encoder for the final architecture.

Source	FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com