Sample specificity learning aims to treat every single sample as a separate class and mine the underlying class-to-class visual similarity relationship, thus learning discriminative feature embeddings without using category labels. We introduce a correlational instance feature embedding approach to improve the representation ability of deep neural networks. It exploits the self-correlation and cross-correlation of instances in each training batch by learning a feature embedding with intrainstance variation and interinstance interpolation, resulting in stronger discriminability and better generalizability. The exhaustive experiments on several benchmarks show the performance advantages of our proposed method over the existing methods.