Establishing dense semantic correspondences requires dealing with large geometric variations caused by the unconstrained setting of images. To address such severe matching ambiguities, we introduce a novel approach, called guided semantic flow, based on the key insight that sparse yet reliable matches can effectively capture non-rigid geometric variations, and these confident matches can guide adjacent pixels to have similar solution spaces, reducing the matching ambiguities significantly. We realize this idea with learning-based selection of confident matches from an initial set of all pairwise matching scores and their propagation by a new differentiable upsampling layer based on moving least square concept. We take advantage of the guidance from reliable matches to refine the matching hypotheses through Gaussian parametric model in the subsequent matching pipeline. With the proposed method, state-of-the-art performance is attained on several standard benchmarks for semantic correspondence.