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Learning representations in an unsupervised or self-supervised manner is a growing area of research. Current approaches in representation learning seek to maximize the mutual information between the learned representation and original data. One of the most popular ways to estimate mutual information (MI) is based on Noise Contrastive Estimation (NCE). This MI estimate exhibits low variance, but it is upper-bounded by log(N), where N is the number of samples. In an ideal scenario, we would usedoi:10.24963/ijcai.2020/385 dblp:conf/ijcai/YuSAP20 fatcat:sdf77ztcpre4bfc6wdssxpcfcm