4.7 Article

A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned, and Perspectives

Related references

Note: Only part of the references are listed.
Proceedings Paper Computer Science, Artificial Intelligence

Leveraging Latent Features for Local Explanations

Ronny Luss et al.

Summary: This paper explores a new approach to explaining the decisions of deep neural networks by generating contrastive explanations using latent features. This method highlights aspects sufficient to justify classification and also introduces new aspects that can alter the classification.

KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2021)

Review Engineering, Multidisciplinary

A Survey on Contrastive Self-Supervised Learning

Ashish Jaiswal et al.

Summary: Self-supervised learning, particularly through contrastive learning, has gained popularity for its cost-effective approach in using self-defined pseudolabels for various downstream tasks. This paper extensively reviews self-supervised methods following the contrastive approach, explaining pretext tasks and different architectures used. Performance comparisons across multiple downstream tasks demonstrate variations in method effectiveness.

TECHNOLOGIES (2021)