Invention Application
- Patent Title: SYSTEMS AND METHODS FOR INTERPOLATIVE CENTROID CONTRASTIVE LEARNING
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Application No.: US17188232Application Date: 2021-03-01
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Publication No.: US20220156530A1Publication Date: 2022-05-19
- Inventor: Anthony Meng Huat Tiong , Junnan Li , Chu Hong Hoi
- Applicant: salesforce.com, inc.
- Applicant Address: US CA San Francisco
- Assignee: salesforce.com, inc.
- Current Assignee: salesforce.com, inc.
- Current Assignee Address: US CA San Francisco
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06N3/08

Abstract:
An interpolative centroid contrastive learning (ICCL) framework is disclosed for learning a more discriminative representation for tail classes. Specifically, data samples, such as natural images, are projected into a low-dimensional embedding space, and class centroids for respective classes are created as average embeddings of samples that belong to a respective class. Virtual training samples are then created by interpolating two images from two samplers: a class-agnostic sampler which returns all images from both the head class and the tail class with an equal probability, and a class-aware sampler which focuses more on tail-class images by sampling images from the tail class with a higher probability compared to images from the head class. The sampled images, e.g., images from the class-agnostic sampler and images from the class-aware sampler may be interpolated to generate interpolated images.
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