Summary of Semi-supervised Learning For Deep Causal Generative Models, by Yasin Ibrahim et al.
Semi-Supervised Learning for Deep Causal Generative Models
by Yasin Ibrahim, Hermione Warr, Konstantinos Kamnitsas
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents a novel approach to developing causal generative models that can answer complex medical image analysis questions. The authors aim to address the limitations of existing models by creating a semi-supervised deep learning framework that utilizes causal relationships between variables to maximize the use of available data, even when some samples are incomplete or unlabeled. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand how someone’s health would change if they had taken a different medication. Medical image analysis is crucial for answering such questions. However, current models require complete records and labels for each patient, which is not always possible. This research fills the gap by introducing a new deep learning model that can use all available data, even when some parts are missing. |
Keywords
* Artificial intelligence * Deep learning * Semi supervised
