Supplementary Material for Paper 1 " Universal Semi-Supervised Learning " 2

Moreover, we will conduct additional experiments to further evaluate our method in Section C. Furthermore, we provide the standard deviation results that correspond to the main paper in Section D. Finally, we will discuss the limitations and social impact of our method in Section E. VisDA2017 datasets, we set the batch size to 64. Other implementation details are presented below. It contains 3 domains: "Amazon" (A), "DSLR" (D), and "Webcam" (W), and each domain is composed of 31 classes. Shared learning rate decay factor 0.2 # training iteration in which learning rate decay starts 400,000 # training iteration in which consistency coefficient ramp up starts 200,000 Supervised Initial learning rate 0.003 Π-Model [6, 10] Initial learning rate 3 10 CAFA framework, which includes class-sharing data detection and feature adaptation . Here we use PI as the backbone method.