Title: Learned Nearest-Class-Mean for Biased Representations in Long-Tailed Recognition. Abstract: The problem of long-tailed recognition (LTR) has received attention in recent years due to the fundamental power-law distribution of objects in the real-world. While classifier bias in LTR has been addressed by many works, representation bias has not yet been researched. At the same time, most recent works use softmax classifiers that are unable to cope with representation bias. In this work, we address these shortcomings by firstly making the key observation that intra-class variance in representation space is negatively correlated to class frequency, leading to biased representations; our analysis reveals that high tail variance is due to spurious correlations learned by deep models. Secondly, to counter representation bias, we propose the Learned Nearest-Class-Mean (NCM), which overcomes uncertainty in empirical centroid estimates and jointly learns centroids minimizing average class-distance normalized variance. Further, we adapt the logit adjustment technique in the NCM framework to achieve higher tail class margin. Our Learned NCM with Logit Adjustment achieves 6\% gain over state-of-the-art in tail accuracy on the benchmark CIFAR100-LT and ImageNet-LT datasets. 