SPACE SHUTTLE MODEL: A PHYSICS INSPIRED METHOD FOR LEARNING QUANTIZABLE DEEP REPRESENTATIONS

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 both strongly preserve similarities between data pairs and can be accurately quantized via vector quantization remains a challenging task. In this paper, we propose a novel physics based method named space shuttle model (SSM) to learn effective deep representations that can be accurately quantized. It consider network output as a roaming space shuttle "propelled" by similarity loss and subject to "gravitational forces" from quantization codewords. SSM is related to momentum methods commonly used in deep leaming but is applied on network outputs instead of network parameters. Experimental results on large scale similarity search demonstrate that the proposed framework outperforms the state-of-the-art.