Accurate Deep Representation Quantization with Gradient Snapping Layer for Similarity Search

TL;DR

This paper introduces a Gradient Snapping Layer (GSL) that improves deep representation learning for similarity search by reducing bias in gradients, leading to more accurate quantization and better search performance.

Contribution

The paper proposes a novel GSL that regularizes gradients towards codewords, enhancing deep representation quality for similarity search, compatible with various quantization methods.

Findings

Outperforms state-of-the-art similarity search methods

Effectively reduces gradient bias in deep representation learning

Compatible with multiple vector quantization approaches

Abstract

Recent advance of large scale similarity search involves using deeply learned representations to improve the search accuracy and use vector quantization methods to increase the search speed. However, how to learn deep representations that strongly preserve similarities between data pairs and can be accurately quantized via vector quantization remains a challenging task. Existing methods simply leverage quantization loss and similarity loss, which result in unexpectedly biased back-propagating gradients and affect the search performances. To this end, we propose a novel gradient snapping layer (GSL) to directly regularize the back-propagating gradient towards a neighboring codeword, the generated gradients are un-biased for reducing similarity loss and also propel the learned representations to be accurately quantized. Joint deep representation and vector quantization learning can be easily performed by alternatively optimize the quantization codebook and the deep neural network. The proposed framework is compatible with various existing vector quantization approaches. Experimental results demonstrate that the proposed framework is effective, flexible and outperforms the state-of-the-art large scale similarity search methods.