Summary of How Green Is Continual Learning, Really? Analyzing the Energy Consumption in Continual Training Of Vision Foundation Models, by Tomaso Trinci et al.
How green is continual learning, really? Analyzing the energy consumption in continual training of vision foundation models
by Tomaso Trinci, Simone Magistri, Roberto Verdecchia, Andrew D. Bagdanov
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 abstract presents research on the environmental sustainability of AI’s continual learning algorithms. The study compares the energy consumption of various algorithms and standard baselines when used to continually adapt a pre-trained foundation model on three datasets. The authors propose a novel metric, Energy NetScore, to measure the algorithm efficiency in terms of energy-accuracy trade-off. Results show that different algorithms have varying impacts on energy consumption during training and inference, with the inference phase being crucial for evaluating environmental sustainability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research focuses on making AI more environmentally friendly by understanding the energy efficiency of continual learning algorithms. The scientists compare different ways to update an existing AI model with new data, finding that some methods use much more energy than others. They also created a new way to measure how well these algorithms balance energy usage and accuracy. This study will help us make AI more sustainable for the planet. |
Keywords
» Artificial intelligence » Continual learning » Inference
