Summary of Robust Few-shot Ensemble Learning with Focal Diversity-based Pruning, by Selim Furkan Tekin et al.
Robust Few-Shot Ensemble Learning with Focal Diversity-Based Pruning
by Selim Furkan Tekin, Fatih Ilhan, Tiansheng Huang, Sihao Hu, Ka-Ho Chow, Margaret L. Loper, Ling Liu
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary FusionShot is a novel few-shot learning approach that combines multiple pre-trained models to improve their robustness and generalization performance. The method, which optimizes focal diversity, creates three alternative fusion channels and learns the most efficient ensemble teaming strategy by pruning out candidate ensembles with low error diversity. A learn-to-combine algorithm is also designed to capture complex non-linear patterns in ensemble predictions. Experimental results on representative few-shot benchmarks show that FusionShot can outperform state-of-the-art models on novel tasks and prevail over existing learners in cross-domain and adversarial settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FusionShot is a way to make machine learning models better at learning new things from just a few examples. It’s like a team effort, where multiple models work together to get smarter. The approach makes sure that each model has its own special strengths, so they can combine their skills to do really well on new tasks. This helps the models be more accurate and less likely to make mistakes when trying something new. |
Keywords
» Artificial intelligence » Few shot » Generalization » Machine learning » Pruning
