Online sketching hashing

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Quantized Convolutional Neural Networks for Mobile Devices (2015, methodology): “Product quantization [14] is widely used in approximate nearest neighbor search, demonstrating better performance than hashing-based methods [21, 22].”
A Survey on Learning to Hash (2016, background): “There are some approaches to learn hash functions without such assumptions: active hashing [176] that actively selects the pairs which are most informative for hash function learning and labels them for further learning, online hashing [43], smart hashing [163], online sketching hashing [69], and on”
Complementary Incremental Hashing With Query-Adaptive Re-Ranking for Image Retrieval (2020, methodology): “The OSH [41] method learns hash functions based on the sketch of database, which could be updated dynamically with new data appearing over time.”
Hashing for Distributed Data (2015, methodology): “In this work, inspired by (Norouzi & Fleet, 2013), we restrict the codeword to be an additive combination of the columns of a dictionary matrix C ∈ Rd×r. Formally, let b(x) be the r-bit binary hashing code of x, then the corresponding assigned codeword is Cb(x).”