**Meta Face Recognition** (MFR) is a meta-learning face recognition method. MFR synthesizes the source/target domain shift with a meta-optimization objective, which requires the model to learn effective representations not only on synthesized source domains but also on synthesized target domains. Specifically, domain-shift batches are built through a domain-level sampling strategy and back-propagated gradients/meta-gradients are obtained on synthesized source/target domains by optimizing multi-domain distributions. The gradients and meta-gradients are further combined to update the model to improve generalization.

**GENets**, or **GPU-Efficient Networks**, are a family of efficient models found through [neural architecture search](https://paperswithcode.com/methods/category/neural-architecture-search). The search occurs over several types of convolutional block, which include [depth-wise convolutions](https://paperswithcode.com/method/depthwise-convolution), [batch normalization](https://paperswithcode.com/method/batch-normalization), [ReLU](https://paperswithcode.com/method/relu), and an [inverted bottleneck](https://paperswithcode.com/method/inverted-residual-block) structure.

GENet

Neural Architecture Design for GPU-Efficient Networks

Learning Meta Face Recognition in Unseen Domains

Attention gate focuses on targeted regions while suppressing feature activations in irrelevant regions.
Given the input feature map $X$ and the gating signal $G\in \mathbb{R}^{C'\times H\times W}$ which is collected at a coarse scale and contains contextual information, the attention gate uses additive attention to obtain the gating coefficient. Both the input $X$ and the gating signal are first linearly mapped to an $\mathbb{R}^{F\times H\times W}$ dimensional space, and then the output is squeezed in the channel domain to produce a spatial attention weight map $ S \in \mathbb{R}^{1\times H\times W}$. The overall process can be written as
\begin{align}
    S &= \sigma(\varphi(\delta(\phi_x(X)+\phi_g(G))))
\end{align}
\begin{align}
    Y &= S X
\end{align}
where $\varphi$, $\phi_x$ and $\phi_g$ are linear transformations implemented as $1\times 1$ convolutions. 

The attention gate guides the model's attention to important regions while suppressing feature activation in unrelated areas. It substantially enhances the representational power of the model without a significant increase in computing cost or number of model parameters due to its lightweight design. It is general and modular, making it simple to use in various CNN models.

Source	Learning Meta Face Recognition in Unseen Domains
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com