Summary of Resque: Quantifying Estimator to Task and Distribution Shift For Sustainable Model Reusability, by Vishwesh Sangarya and Jung-eun Kim
RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability
by Vishwesh Sangarya, Jung-Eun Kim
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 The proposed RESQUE (REpresentation Shift QUantifying Estimator) is a predictive quantifier that estimates the retraining cost of a deep learning model when adapting to distributional shifts or changes in tasks. RESQUE provides a single index to predict the resources required for retraining, which correlates strongly with various retraining measures such as epochs, gradient norms, and energy consumption. This allows users to make informed decisions about retraining models for different tasks or distributions, ultimately reducing environmental impact. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach an AI model new tricks. Instead of starting from scratch, you can reuse the model by adjusting it to fit new tasks or data sets. The problem is, you don’t know how much effort and resources this will require. A team of researchers has developed a way to estimate this retraining cost in advance. This approach, called RESQUE, helps you decide whether to adapt an existing model or start fresh. It’s like knowing how many steps it’ll take to get to your destination before you even start walking! |
Keywords
» Artificial intelligence » Deep learning
