An **Efficient Recurrent Unit (ERU)** extends [LSTM](https://paperswithcode.com/method/mrnn)-based language models by replacing linear transforms for processing the input vector with the [EESP](https://paperswithcode.com/method/eesp) unit inside the [LSTM](https://paperswithcode.com/method/lstm) cell.

**SFAM**, or **Scale-wise Feature Aggregation Module**, is a feature extraction block from the [M2Det](https://paperswithcode.com/method/m2det) architecture. It aims to aggregate the multi-level multi-scale features generated by [Thinned U-Shaped Modules](https://paperswithcode.com/method/tum) into a multi-level feature pyramid. 

The first stage of SFAM is to concatenate features of the equivalent scale together along the channel dimension. The aggregated feature pyramid can be presented as $\mathbf{X} =[\mathbf{X}\_1,\mathbf{X}\_2,\dots,\mathbf{X}\_i]$, where $\mathbf{X}\_i = \text{Concat}(\mathbf{x}\_i^1,\mathbf{x}\_i^2,\dots,\mathbf{x}\_i^L) \in \mathbb{R}^{W\_{i}\times H\_{i}\times C}$ refers to the features of the $i$-th largest scale. Here, each scale in the aggregated pyramid contains features from multi-level depths. 

However, simple concatenation operations are not adaptive enough. In the second stage, we introduce a channel-wise attention module to encourage features to focus on channels that they benefit most. Following Squeeze-and-Excitation, we use [global average pooling](https://paperswithcode.com/method/global-average-pooling) to generate channel-wise statistics $\mathbf{z} \in \mathbb{R}^C$ at the squeeze step. And to fully capture channel-wise dependencies, the following excitation step learns the attention mechanism via two fully connected layers:

$$
\mathbf{s} = \mathbf{F}\_{ex}(\mathbf{z},\mathbf{W}) = \sigma(\mathbf{W}\_{2} \delta(\mathbf{W}\_{1}\mathbf{z})),
$$

where $\sigma$ refers to the [ReLU](https://paperswithcode.com/method/relu) function, $\delta$ refers to the sigmoid function, $\mathbf{W}\_{1} \in \mathbb{R}^{\frac{C}{r}\times C}$ , $\mathbf{W}\_{2} \in \mathbb{R}^{C\times \frac{C}{r}}$, r is the reduction ratio ($r=16$ in our experiments). The final output is obtained by reweighting the input $\mathbf{X}$ with activation $\mathbf{s}$:

$$
\tilde{\mathbf{X}}_i^c = \mathbf{F}\_{scale}(\mathbf{X}\_i^c,s_c) = s_c \cdot \mathbf{X}_i^c,
$$

where $\tilde{\mathbf{X}\_i} = [\tilde{\mathbf{X}}\_i^1,\tilde{\mathbf{X}}\_i^2,...,\tilde{\mathbf{X}}\_i^C]$, each of the features is enhanced or weakened by the rescaling operation.

SFAM

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network

**Electric** is an energy-based cloze model for representation learning over text. Like BERT, it is a conditional generative model of tokens given their contexts. However, Electric does not use masking or output a full distribution over tokens that could occur in a context. Instead, it assigns a scalar energy score to each input token indicating how likely it is given its context.

Specifically, like BERT, Electric also models $p\_{\text {data }}\left(x\_{t} \mid \mathbf{x}\_{\backslash t}\right)$, but does not use masking or a softmax layer. Electric first maps the unmasked input $\mathbf{x}=\left[x\_{1}, \ldots, x\_{n}\right]$ into contextualized vector representations $\mathbf{h}(\mathbf{x})=\left[\mathbf{h}\_{1}, \ldots, \mathbf{h}\_{n}\right]$ using a transformer network. The model assigns a given position $t$ an energy score

$$
E(\mathbf{x})\_{t}=\mathbf{w}^{T} \mathbf{h}(\mathbf{x})\_{t}
$$

using a learned weight vector $w$. The energy function defines a distribution over the possible tokens at position $t$ as

$$
p\_{\theta}\left(x\_{t} \mid \mathbf{x}_{\backslash t}\right)=\exp \left(-E(\mathbf{x})\_{t}\right) / Z\left(\mathbf{x}\_{\backslash t}\right) 
$$

$$
=\frac{\exp \left(-E(\mathbf{x})\_{t}\right)}{\sum\_{x^{\prime} \in \mathcal{V}} \exp \left(-E\left(\operatorname{REPLACE}\left(\mathbf{x}, t, x^{\prime}\right)\right)\_{t}\right)}
$$

where $\text{REPLACE}\left(\mathbf{x}, t, x^{\prime}\right)$ denotes replacing the token at position $t$ with $x^{\prime}$ and $\mathcal{V}$ is the vocabulary, in practice usually word pieces. Unlike with BERT, which produces the probabilities for all possible tokens $x^{\prime}$ using a softmax layer, a candidate $x^{\prime}$ is passed in as input to the transformer. As a result, computing $p_{\theta}$ is prohibitively expensive because the partition function $Z\_{\theta}\left(\mathbf{x}\_{\backslash t}\right)$ requires running the transformer $|\mathcal{V}|$ times; unlike most EBMs, the intractability of $Z\_{\theta}(\mathbf{x} \backslash t)$ is more due to the expensive scoring function rather than having a large sample space.

Source	ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com