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15 October 2024 |
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Article overview
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Negative Sampling for Contrastive Representation Learning: A Review | | Lanling Xu
; Jianxun Lian
; Wayne Xin Zhao
; Ming Gong
; Linjun Shou
; Daxin Jiang
; Xing Xie
; Ji-Rong Wen
; | | Date: |
1 Jun 2022 | | Abstract: | The learn-to-compare paradigm of contrastive representation learning (CRL),
which compares positive samples with negative ones for representation learning,
has achieved great success in a wide range of domains, including natural
language processing, computer vision, information retrieval and graph learning.
While many research works focus on data augmentations, nonlinear
transformations or other certain parts of CRL, the importance of negative
sample selection is usually overlooked in literature. In this paper, we provide
a systematic review of negative sampling (NS) techniques and discuss how they
contribute to the success of CRL. As the core part of this paper, we summarize
the existing NS methods into four categories with pros and cons in each genre,
and further conclude with several open research questions as future directions.
By generalizing and aligning the fundamental NS ideas across multiple domains,
we hope this survey can accelerate cross-domain knowledge sharing and motivate
future researches for better CRL. | | Source: | arXiv, 2206.00212 | | Services: | Forum | Review | PDF | Favorites | |
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