![]() ![]() ![]() ![]() We estimate that 7% of Internet traffic is now QUIC. QUIC has been globally deployed at Google on thousands of servers and is used to serve traffic to a range of clients including a widely-used web browser (Chrome) and a popular mobile video streaming app (YouTube). We present our experience with QUIC, an encrypted, multiplexed, and low-latency transport protocol designed from the ground up to improve transport performance for HTTPS traffic and to enable rapid deployment and continued evolution of transport mechanisms. Langley, A Riddoch, A Wilk, A Vicente, A Krasic, C Zhang, D Yang, F Kouranov, F Swett, I Iyengar, J Bailey, J Dorfman, J Roskind, J Kulik, J Westin, P Tenneti, R Shade, R Hamilton, R Vasiliev, V Chang, WT Shi, ZY 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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