Learning from a few examples is a challenging computer vision task. Traditionally,meta-learning-based methods have shown promise towards solving this problem.Recent approaches show benefits by learning a feature extractor on the abundantbase examples and transferring these to the fewer novel examples. However, thefinetuning stage is often prone to overfitting due to the small size of the noveldataset. To this end, we propose Few shot Learning with hard Mixup (FeLMi)using manifold mixup to synthetically generate samples that helps in mitigatingthe data scarcity issue. Different from a naïve mixup, our approach selects the hardmixup samples using an uncertainty-based criteria. To the best of our knowledge,we are the first to use hard-mixup for the few-shot learning problem. Our approachallows better use of the pseudo-labeled base examples through base-novel mixupand entropy-based filtering. We evaluate our approach on several common few-shotbenchmarks - FC-100, CIFAR-FS, miniImageNet and tieredImageNet and obtainimprovements in both 1-shot and 5-shot settings. Additionally, we experimented onthe cross-domain few-shot setting (miniImageNet → CUB) and obtain significantimprovements.