Title: Lifting Imbalanced Regression with Self-Supervised Learning. Abstract: A new influential task called imbalanced regression, most recently inspired by imbalanced classification, originating straightforwardly from both the imbalance and regression worlds, has received a great deal of attention. Yet we are still at a fairly preliminary stage in the exploration of this task, so more attempts are needed. In this paper, we work on a seamless marriage of imbalanced regression and self-supervised learning. But with this comes the first question of how to measure the similarity and dissimilarity under the regression sense, for which the definition is clear in the classification. To overcome the limitation, the formal definition of similarity in the regression task is given. On top of this, through experimenting on a simple neural network, we found that self-supervised learning could help alleviate the problem. However, the second problem is, it is not guaranteed that the noisy samples are similar to original samples when scaling to a deep network by adding random noise to the input, we specifically propose to limit the volume of noise on the output, and in doing so to find meaningful noise on the input by back propagation. Experimental results show that our approach achieves the state-of-the-art performance.