Figure 5:

Learning artifact representations $\eta \in {ℝ}^2$ with contrastive learning. $x_q$ and $x_{+}$ are assumed to have the same level of artifacts, while $x_q$ and $x_{-}^{(m)}$ have different levels of artifacts. The contrastive loss is applied to encourage $\eta$ to preserve this relationship.