Abstract. Triplet loss, popular for metric learning, has made a great success in many computer vision tasks, such as fine-grained image classification, image retrieval, and face recognition. Considering that the number of triplets grows cubically with the size of training data, triplet selection is thus indispensable for efficiently training with triplet loss. However, in practice, the training is usually very sensitive to the selection of triplets, e.g., it almost does not converge with randomly selected triplets and selecting the hardest triplets also leads to bad local minima. We argue that the bias in the selection of triplets degrades the performance of learning with triplet loss. In this paper, we propose a new variant of triplet loss, which tries to reduce the bias in triplet selection by adaptively correcting the distribution shift on the selected triplets. We refer to this new triplet loss as adapted triplet loss. We conduct a number of experiments on MNIST and Fashion-MNIST for image classification, and on CARS196, CUB200-2011, and Stanford Online Products for image retrieval. The experimental results demonstrate the effectiveness of the proposed method