Title: Differentiable Channel Selection for Self-Attention. Abstract: Self-attention has been widely used in deep learning, and recent efforts have been devoted to incorporating self-attention modules into convolutional neural networks for computer vision. In this paper, we propose a novel attention module termed Differentiable Channel Selection (DCS). In contrast with conventional self-attention, DCS searches for the locations and key dimension of channels in a continuous space by a novel differentiable searching method. Our DCS module is compatible with either fixed neural network backbone or learnable backbone with Differentiable Neural Architecture Search (DNAS), leading to DCS with Fixed Backbone (DCS-FB) and DCS-DNAS respectively. We apply DCS-FB and DCS-DNAS to three computer vision tasks, person Re-IDentification methods (Re-ID), object detection, and image classification, with state-of-the-art results on standard benchmarks and compact architecture compared to competing methods, revealing the advantage of DCS.