**Early Stopping** is a regularization technique for deep neural networks that stops training when parameter updates no longer begin to yield improves on a validation set. In essence, we store and update the current best parameters during training, and when parameter updates no longer yield an improvement (after a set number of iterations) we stop training and use the last best parameters. It works as a regularizer by restricting the optimization procedure to a smaller volume of parameter space.

Image Source: [Ramazan Gençay](https://www.researchgate.net/figure/Early-stopping-based-on-cross-validation_fig1_3302948)

**Attentional Liquid Warping Block**, or **AttLWB**, is a module for human image synthesis GANs that propagates the source information - such as texture, style, color and face identity - in both image and feature spaces to the synthesized reference. It firstly learns similarities of the global features among all multiple sources features, and then it fuses the multiple sources features by a linear combination of the learned similarities and the multiple sources in the feature spaces. Finally, to better propagate the source identity (style, color, and texture) into the global stream, the fused source features are warped to the global stream by [Spatially-Adaptive Normalization](https://paperswithcode.com/method/spade) (SPADE).

AttLWB

Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis

Early Stopping

FIERCE is an entropic regularization on the **feature** space

Year	1995
Data Source	CC BY-SA - https://paperswithcode.com