The QUIC Transport Protocol: Design and Internet-Scale Deployment Our method achieves state-of-the-art performance on all of them. We evaluate our approach on the Stanford Online Products, CAR196, and the CUB200-2011 datasets for image retrieval and clustering, and on the LFW dataset for face verification. In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions. We propose distance weighted sampling, which selects more informative and stable examples than traditional approaches. While a rich line of work focuses solely on the loss functions, we show in this paper that selecting training examples plays an equally important role. The most prominent approaches optimize a deep convolutional network with a suitable loss function, such as contrastive loss or triplet loss. Wu, CY Manmatha, R Smola, AJ Krahenbuhl, PÄeep embeddings answer one simple question: How similar are two images? Learning these embeddings is the bedrock of verification, zero-shot learning, and visual search. Sampling Matters in Deep Embedding Learning
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