**Self-Cure Network**, or **SCN**, is a method for suppressing uncertainties for large-scale facial expression recognition, prventing deep networks from overfitting uncertain facial images. Specifically, SCN suppresses the uncertainty from two different aspects: 1) a self-attention mechanism over mini-batch to weight each training sample with a ranking regularization, and 2) a careful relabeling mechanism to modify the labels of these samples in the lowest-ranked group.

The methon to overcome catastrophic forgetting in neural network while continual learning

Overcoming catastrophic forgetting in neural networks

Suppressing Uncertainties for Large-Scale Facial Expression Recognition

A **Switch FFN** is a sparse layer that operates independently on tokens within an input sequence. It is shown in the blue block in the figure. We diagram two tokens ($x\_{1}$ = “More” and $x\_{2}$ = “Parameters” below) being routed (solid lines) across four FFN experts, where the router independently routes each token. The switch FFN layer returns the output of the selected FFN multiplied by the router gate value (dotted-line).

Source	Suppressing Uncertainties for Large-Scale Facial Expression Recognition
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com