AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning (Supplementary Material) Ximeng Sun 1 Rameswar Panda

Tiny Taskonomy consists of 381,840 indoor images from 35 buildings with annotations available for 26 tasks. It is one of the large-scale domain adaptation benchmark with 0.6m images across six Our training is separated into two phases: the Policy Learning Phase and the Re-training Phase. In both phases, we use the early stop to get the best performance during the training. We use the same parameter set for our model and baselines. For Cross-Stitch and Sluice, we insert the linear feature fusion layers after each residual block.