Accurate Deep Representation Quantization with Gradient Snapping Layer for Similarity Search
This addresses the problem of efficient and accurate large-scale similarity search for applications like image retrieval, but it is incremental as it builds on existing vector quantization approaches.
The paper tackles the challenge of learning deep representations that preserve similarity and can be accurately quantized for similarity search, proposing a gradient snapping layer to regularize gradients, which results in improved search performance over state-of-the-art methods.
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.