Supplementary Material SSAL: Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection

In this supplementary material, following sections are discussed: we include training algorithm (Sec. We show the impact on performance of our method with different dropout (spatial [6]) rates in Tab. 1. Our method mostly retains performance when perturbing the dropout rate from 10% to 30%. In particular, we see a maximum decrease of 0.8% in mAP score when increasing the dropout rate from 10% to 30%. This is expected as increasing the dropout rate increases prediction uncertainty which in turn affects the pseudo-label selection.