Gaussian Noise as Surrogate Source Domain Improves Semi-Supervised Transfer Learning

Semi-Supervised Noise Adaptation (SSNA) formalizes using synthetic Gaussian noise distributions as a surrogate source domain for transfer learning when only a small fraction of target samples are labeled. The finding that structureless noise can facilitate effective knowledge transfer challenges assumptions about what constitutes a useful source domain.