Invention Grant
- Patent Title: Multiscale quantization for fast similarity search
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Application No.: US18081376Application Date: 2022-12-14
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Publication No.: US11874866B2Publication Date: 2024-01-16
- Inventor: Xiang Wu , David Simcha , Daniel Holtmann-Rice , Sanjiv Kumar , Ananda Theertha Suresh , Ruiqi Guo , Xinnan Yu
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: GOOGLE LLC
- Current Assignee: GOOGLE LLC
- Current Assignee Address: US CA Mountain View
- Agency: Dority & Manning, P.A.
- Main IPC: G06F16/33
- IPC: G06F16/33 ; G06F16/31 ; G06N20/00

Abstract:
The present disclosure provides systems and methods that include or otherwise leverage use of a multiscale quantization model that is configured to provide a quantized dataset. In particular, the multiscale quantization model can receive and perform vector quantization of a first dataset. The multiscale quantization model can generate a residual dataset based at least in part on a result of the vector quantization. The multiscale quantization model can apply a rotation matrix to the residual dataset to generate a rotated residual dataset that includes a plurality of rotated residuals. The multiscale quantization model can perform reparameterization of each rotated residual in the rotated residual dataset into a direction component and a scale component. The multiscale quantization model can perform product quantization of the direction components of the plurality of rotated residuals, and perform scalar quantization of the scale components of the plurality of rotated residuals.
Public/Granted literature
- US20230123941A1 Multiscale Quantization for Fast Similarity Search Public/Granted day:2023-04-20
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