**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.

**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.

FLAVR

FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation

BigBiGAN

Large Scale Adversarial Representation Learning

The **Lovasz-Softmax loss** is a loss function for multiclass semantic segmentation that incorporates the [softmax](https://paperswithcode.com/method/softmax) operation in the Lovasz extension. The Lovasz extension is a means by which we can achieve direct optimization of the mean intersection-over-union loss in neural networks.

Source	Large Scale Adversarial Representation Learning
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com