Title: Learning in Compressed Domain via Knowledge Transfer. Abstract: Learning in compressed domain aims to perform vision tasks directly on compressed latent representations instead of reconstructed images. Existing reports show that learning in compressed domain can achieve a comparable performance compared to that in pixel domain for certain compression models. However, we observe that when using the state-of-the-art learned compression models, the performance gap between compressed-domain and pixel-domain vision tasks is still large due to the lack of some natural inductive biases in pixel-domain convolutional neural networks. In this paper, we attempt to address this problem by transferring knowledge from pixel domain to compressed domain. We first modify neural networks for pixel-domain vision tasks to better suit compressed-domain inputs. In addition, we propose a knowledge transfer loss to narrow the gap between compressed domain and pixel domain. Experimental results on classification and instance segmentation show that the proposed method improves the accuracy of compressed-domain vision tasks significantly, which even outperforms learning on reconstructed images while avoiding the computational cost for image reconstruction.