  With the explosive growth of image databases, deep hashing, which learns
compact binary descriptors for images, has become critical for fast image
retrieval. Many existing deep hashing methods leverage quantization loss,
defined as distance between the features before and after quantization, to
reduce the error from binarizing features. While minimizing the quantization
loss guarantees that quantization has minimal effect on retrieval accuracy, it
unfortunately significantly reduces the expressiveness of features even before
the quantization. In this paper, we show that the above definition of
quantization loss is too restricted and in fact not necessary for maintaining
high retrieval accuracy. We therefore propose a new form of quantization loss
measured in triplets. The core idea of the triplet quantization loss is to
learn discriminative real-valued descriptors which lead to minimal loss on
retrieval accuracy after quantization. Extensive experiments on two widely used
benchmark data sets of different scales, CIFAR-10 and In-shop, demonstrate that
the proposed method outperforms the state-of-the-art deep hashing methods.
Moreover, we show that the compact binary descriptors obtained with triplet
quantization loss lead to very small performance drop after quantization.
