Summary of Addressing Bias Through Ensemble Learning and Regularized Fine-tuning, by Ahmed Radwan et al.
Addressing Bias Through Ensemble Learning and Regularized Fine-Tuning
by Ahmed Radwan, Layan Zaafarani, Jetana Abudawood, Faisal AlZahrani, Fares Fourati
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this research paper, a comprehensive approach is proposed for removing biases in AI models using multiple methods, even when working with small datasets and potentially biased pre-trained models. The authors train multiple models through data splitting, local training, and regularized fine-tuning to obtain counter-biased models, which are then combined using ensemble learning to achieve unbiased predictions. To further accelerate inference time, the team uses knowledge distillation to create a single unbiased neural network. Promising results are demonstrated on the CIFAR10 and HAM10000 datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI models can be biased, leading to unfair and inaccurate predictions. To fix this, scientists have developed an innovative way to remove biases from AI models using only a small dataset and a possibly biased pre-trained model. They train many models in different ways to create counter-biased models, which are then combined to make unbiased predictions. This new approach helps create more reliable AI models, even with limited data. |
Keywords
* Artificial intelligence * Fine tuning * Inference * Knowledge distillation * Neural network
