Using domain adaptation to increase AI model robustness
Project: SIDDA: SInkhorn Dynamic Domain Adaptation for image classification with equivariant neural networks
When a neural network is trained on one set of data, it often doesn’t perform well on a slightly different one. This happens when the underlying patterns of the data change, even if the relationships between the data points remain the same, also known as domain shift. This effect can be reduced using a technique called domain adaptation, or DA, which helps the neural network only learn things about shared inputs in both datasets.
Fermilab is leading the development of domain adaptation algorithms for science. The SIDDA method achieves this without complex adjustments using equivariant neural networks, also reducing computational costs.
