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.

**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.

BigBiGAN

Large Scale Adversarial Representation Learning

Lovasz-Softmax

Stochastic Steady-state Embedding (SSE) is an algorithm that can learn many steady-state algorithms over graphs. Different from graph neural network family models, SSE is trained stochastically which only requires 1-hop information, but can capture fixed point relationships efficiently and effectively.

Description and Image from: [Learning Steady-States of Iterative Algorithms over Graphs](https://proceedings.mlr.press/v80/dai18a.html)

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com