Summary of Calico: Confident Active Learning with Integrated Calibration, by Lorenzo S. Querol et al.
CALICO: Confident Active Learning with Integrated Calibration
by Lorenzo S. Querol, Hajime Nagahara, Hideaki Hayashi
First submitted to arxiv on: 2 Jul 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 Confident Active Learning with Integrated CalibratiOn (CALICO) framework addresses the challenges in training deep neural networks (DNNs) for safety-critical applications with limited labeled data. CALICO utilizes an AL approach that self-calibrates confidence scores during training, allowing for simultaneous estimation of input data distribution and class probabilities. This improves calibration without requiring additional labeled data. The framework incorporates joint training of a classifier and energy-based model, departing from the standard softmax-based classifier. Experimental results demonstrate improved classification performance with fewer labeled samples compared to a softmax-based classifier. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to train deep neural networks using limited labeled data. This is important because some applications like medical imaging need accurate models but don’t have enough labeled data. The solution uses active learning, which chooses the most helpful data points for training. However, this approach requires reliable confidence scores from the model. Since modern models are not very good at predicting their own uncertainty, a new method called CALICO is developed to self-calibrate these confidence scores during training. This results in better calibration without needing more labeled data. The experiment shows that CALICO works well and improves classification accuracy with fewer labeled samples. |
Keywords
* Artificial intelligence * Active learning * Classification * Energy based model * Softmax
