The paper "Sampling Matters in Deep Embedding Learning" by Chao-Yuan Wu, R. Manmatha, Alexander J. Smola, and Philipp Krähenbühl explores the importance of sample selection in deep embedding learning, particularly in image retrieval, clustering, and face verification. The authors argue that while loss functions like contrastive loss and triplet loss are crucial, the choice of training examples is equally important. They propose a new sampling strategy called distance weighted sampling, which selects more informative and stable examples by uniformly sampling according to the relative distance between examples. This approach corrects the bias induced by the geometry of the embedding space and stabilizes training, leading to better performance. Additionally, they introduce a simple margin-based loss function that relaxes the constraints of traditional contrastive loss while maintaining the flexibility of the triplet loss. The proposed methods achieve state-of-the-art performance on various datasets, including Stanford Online Products, CARS196, CUB200-2011, and LFW. The paper also includes a detailed analysis of existing sampling strategies and a comparison with other loss functions, demonstrating the effectiveness of their proposed methods.The paper "Sampling Matters in Deep Embedding Learning" by Chao-Yuan Wu, R. Manmatha, Alexander J. Smola, and Philipp Krähenbühl explores the importance of sample selection in deep embedding learning, particularly in image retrieval, clustering, and face verification. The authors argue that while loss functions like contrastive loss and triplet loss are crucial, the choice of training examples is equally important. They propose a new sampling strategy called distance weighted sampling, which selects more informative and stable examples by uniformly sampling according to the relative distance between examples. This approach corrects the bias induced by the geometry of the embedding space and stabilizes training, leading to better performance. Additionally, they introduce a simple margin-based loss function that relaxes the constraints of traditional contrastive loss while maintaining the flexibility of the triplet loss. The proposed methods achieve state-of-the-art performance on various datasets, including Stanford Online Products, CARS196, CUB200-2011, and LFW. The paper also includes a detailed analysis of existing sampling strategies and a comparison with other loss functions, demonstrating the effectiveness of their proposed methods.