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Making machine learning research accessible through clear writing and engaging visuals

Published on:

9 September 2023

Primary Category:

Computer Vision and Pattern Recognition

Paper Authors:

Hai-Ming Xu,

Lingqiao Liu,

Hao Chen,

Ehsan Abbasnejad,

Rafael Felix

Bullets

Key Details

The original paper describes methods for semi-supervised learning using pretrained models

The authors identify issues with bias when applying standard SSL techniques to pretrained models

A simple progressive feature adjustment method is proposed to address these issues

Experiments show large performance gains over prior SSL techniques when using pretrained models

The method works by updating the feature extractor with pseudo-labels and the classifier with true labels

AI generated summary

Making machine learning research accessible through clear writing and engaging visuals

This paper proposes techniques to make complex machine learning research more accessible to general audiences. The authors craft an alternative title that appeals to a broad readership, provide a plain-language summary, and generate bullet points and FAQs to explain key concepts clearly.

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