MeanRemoved Product Quantization for Approximate Nearest Neighbor Search Product quantization (PQ) and its variations are popular and attractive in approximate nearest neighbor search (ANN) due to their lower memory usage and faster retrieval speed. PQ decomposes the highdimensional vector space into several lowdimensional subspaces, and quantizes each subvector in their subspaces, separately. Thus, PQ can generate a codebook containing an exponential number of codewords or indices by a Cartesian product of the subcodebooks from different subspaces. However, when there is large variance in the average amplitude of the components of the data points, directly utilizing the PQ on the data points would result in poor performance. In this paper, we propose a new approach, namely, meanremoved product quantization (MRPQ) to address this issue. In fact, the average amplitude of a data point or the mean of a date point can be regarded as statistically independent of the variation of the vector, that is, of the way the components vary about this average. Then we can learn a separate scalar quantizer of the means of the data points and apply the PQ to their residual vectors. As shown in our comprehensive experiments on four largescale public datasets, our approach can achieve substantial improvements in terms of Recall and MAP over some known methods. Moreover, our approach is general which can be combined with PQ and its variations.

EndtoEnd Supervised Product Quantization for Image Search and Retrieval. Product Quantization, a dictionary based hashing method, is one of the leading unsupervised hashing techniques. While it ignores the labels, it harnesses the features to construct look up tables that can approximate the feature space. In recent years, several works have achieved state of the art results on hashing benchmarks by learning binary representations in a supervised manner. This work presents Deep Product Quantization (DPQ), a technique that leads to more accurate retrieval and classification than the latest state of the art methods, while having similar computational complexity and memory footprint as the Product Quantization method. To our knowledge, this is the first work to introduce a dictionarybased representation that is inspired by Product Quantization and which is learned endtoend, and thus benefits from the supervised signal. DPQ explicitly learns soft and hard representations to enable an efficient and accurate asymmetric search, by using a straightthrough estimator. Our method obtains state of the art results on an extensive array of retrieval and classification experiments.