Homogenous Transfer Learning

In homogeneous transfer learning, the feature spaces of the source and target domains is of the same dimension (Ds = Dt) while the data of both domains is represented by the same attributes (Xs = Xt) and labels (Ys = Yt). Thus, homogeneous transfer learning aims to bridge the gap in the data distributions experienced during cross-domain transfer.

References

Khalil, K., Asgher, U., & Ayaz, Y. (2022). Novel fNIRS study on homogeneous symmetric feature-based transfer learning for brain-computer interface. Scientific Reports, 12, 3198. Link.