Domain Adaptation
Learning to Match
Introduced by Yu et al. in Learning to Match Distributions for Domain AdaptationL2M is a learning algorithm that can work for most cross-domain distribution matching tasks. It automatically learns the cross-domain distribution matching without relying on hand-crafted priors on the matching loss. Instead, L2M reduces the inductive bias by using a meta-network to learn the distribution matching loss in a data-driven way.
Source: Learning to Match Distributions for Domain Adaptation
Papers
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Usage Over Time
Components
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| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
