EfficientNet function composition or Hadamard In the page 3 of the paper of EfficientNet, there is a equation
$$\mathcal{N} = \bigodot_{i=1...s} \mathcal{F}_{i}^{L_i} \big(X_{\langle H_i, W_i, C_i \rangle}\big)$$
where $\mathcal{N}$ is the conv net and each $\mathcal{F}_i^{L_i}$ is the $i$th-stage layer operator that has length $L_i$.
What I don't understand is, what is this $\odot$ in this equation? Does the author refer to the Hadamard product or does he refer to the function composition? He previously mentioned that $\mathcal{N} = \mathcal{F}_k \odot ... \odot \mathcal{F}_1 (X_1)$, where k is the depth of the net. So I thought it means that $\odot$ is just function composition. But EfficientNet has skip connection. In the keras implementation it uses layers.merge.Multiply() so it can also means that the input data $X$ is multiplied with the transformed $\mathcal{F}(X)$, and $\odot$ maybe means the Hadamard product.
Does anyone knows the answer? Thanks.
It probably represents repeated function composition.
Key reasons:

The authors' wording before this equation is: &quot;a list of composed layers&quot;.
A Hadamard product could not be applied to layers of different sizes, whereas function composition of course can.
Yes, EfficientNet has skip connections, but I think they try to validate function composition via a distinction between &quot;layers&quot; and &quot;stages&quot;. They say that &quot;$F_i^{L_i}$ denotes layer $F_i$ is repeated $L_i$ times in stage $i$&quot;. They also explain that &quot;all layers in each stage share the same architecture. Therefore, I think they put skip-connections in the form of function composition, but it is confusing. I think it's their way of compactly expressing the (often) repeated architecture of skip-connected layers.