Cross-Stitch Network
Tags: #machine learning #multi taskEquation
$$\begin{bmatrix} \tilde{x}^{ij}_{A}\\\tilde{x}^{ij}_{B}\end{bmatrix}=\begin{bmatrix} a_{AA} & a_{AB}\\ a_{BA} & a_{BB} \end{bmatrix}\begin{bmatrix} x^{ij}_{A}\\ x^{ij}_{B} \end{bmatrix}$$Latex Code
\begin{bmatrix} \tilde{x}^{ij}_{A}\\\tilde{x}^{ij}_{B}\end{bmatrix}=\begin{bmatrix} a_{AA} & a_{AB}\\ a_{BA} & a_{BB} \end{bmatrix}\begin{bmatrix} x^{ij}_{A}\\ x^{ij}_{B} \end{bmatrix}
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Introduction
Equation
Latex Code
\begin{bmatrix} \tilde{x}^{ij}_{A}\\\tilde{x}^{ij}_{B}\end{bmatrix}=\begin{bmatrix} a_{AA} & a_{AB}\\ a_{BA} & a_{BB} \end{bmatrix}\begin{bmatrix} x^{ij}_{A}\\ x^{ij}_{B} \end{bmatrix}
Explanation
The cross-stitch unit takes two activation maps xA and xB from previous layer and learns a linear combination of two inputs from previous tasks and combine them into two new representation. The linear combination is controlled by parameter \alpha.
Related Documents
- See below link of paper Cross-stitch Networks for Multi-task Learning for more details.
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