This paper introduces the *triplet network* model, a deep learning approach that learns useful representations by comparing distances between samples. Unlike traditional methods that implicitly learn representations as part of a classification task, the triplet network explicitly aims to learn a metric embedding. The model is inspired by the Siamese network but is tailored to learn a ranking function for image retrieval. The authors demonstrate that their model outperforms the Siamese network on various datasets, including Cifar10, MNIST, SVHN, and STL10. They also discuss the potential of the triplet network for unsupervised learning, suggesting its application in scenarios where spatial and temporal information can be used to guide the learning process. The results show that the triplet network can achieve high classification accuracy without data augmentation, and the learned representations are sparse and useful for subsequent classification tasks. The paper concludes by highlighting the potential of the triplet network for further research, particularly in unsupervised learning and the integration of additional data sources.This paper introduces the *triplet network* model, a deep learning approach that learns useful representations by comparing distances between samples. Unlike traditional methods that implicitly learn representations as part of a classification task, the triplet network explicitly aims to learn a metric embedding. The model is inspired by the Siamese network but is tailored to learn a ranking function for image retrieval. The authors demonstrate that their model outperforms the Siamese network on various datasets, including Cifar10, MNIST, SVHN, and STL10. They also discuss the potential of the triplet network for unsupervised learning, suggesting its application in scenarios where spatial and temporal information can be used to guide the learning process. The results show that the triplet network can achieve high classification accuracy without data augmentation, and the learned representations are sparse and useful for subsequent classification tasks. The paper concludes by highlighting the potential of the triplet network for further research, particularly in unsupervised learning and the integration of additional data sources.