Improving Pseudo-Label Learning with Calibrated Confidence Using an Energy-based Model

Temel Kavramlar

Utilizing an energy-based model jointly trained with a classifier to improve confidence calibration and enhance the effectiveness of pseudo-label learning.

Özet

The paper proposes an energy-based pseudo-label learning (EBPL) algorithm that leverages an energy-based model (EBM) to improve confidence calibration and the effectiveness of pseudo-label learning.
Key highlights:

In pseudo-label learning, accurate confidence scores are crucial for selecting appropriate samples to assign pseudo-labels. However, deep neural networks often suffer from over-confidence issues, leading to poor confidence calibration.
EBPL addresses this by jointly training an NN-based classifier and an EBM, which share their feature extraction parts. This allows the model to learn both the class decision boundary and the input data distribution, enhancing confidence calibration.
The experimental results on image classification tasks demonstrate that EBPL outperforms existing pseudo-label learning methods in terms of accuracy, F-score, and expected calibration error. EBPL is particularly effective when the number of labeled data is extremely limited.
Qualitative analysis shows that EBPL assigns more appropriate pseudo-labels by producing lower confidence scores for misclassified samples compared to the baseline method.

İstatistikler

The accuracy of pseudo-labeling at each step is higher for EBPL compared to the baseline method across the CIFAR-10 and Blood-MNIST datasets.

Alıntılar

"By referring to calibrated confidence, we can assign more accurate pseudo-labels, leading to more successful PL."
"EBPL demonstrated higher PL accuracy throughout the entire training process."

Önemli Bilgiler Şuradan Elde Edildi

Pseudo-label Learning with Calibrated Confidence Using an Energy-based Model

by Masahito Tob... : arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09585.pdf

Daha Derin Sorular

How can the proposed EBPL approach be extended to handle larger input image sizes and more complex datasets?

To extend the EBPL approach to handle larger input image sizes and more complex datasets, several strategies can be implemented. One approach is to optimize the training process by utilizing parallel computing resources to speed up the sampling-based gradient estimation required by the energy-based model. This can help reduce the computational cost associated with larger image sizes. Additionally, implementing more efficient sampling techniques, such as advanced Markov chain Monte Carlo methods or stochastic gradient Langevin dynamics, can improve the scalability of the model to handle larger datasets.
Furthermore, incorporating techniques like mini-batch processing and data augmentation can help manage the increased complexity of larger datasets. By dividing the dataset into smaller batches during training, the model can process the data more efficiently. Data augmentation techniques, such as rotation, scaling, and flipping, can also help increase the diversity of the training data, leading to better generalization and performance on complex datasets.

What other applications beyond pseudo-label learning could benefit from the joint training of a classifier and an energy-based model?

The joint training of a classifier and an energy-based model can benefit various applications beyond pseudo-label learning. One such application is anomaly detection, where the model can learn the normal data distribution and identify anomalies based on deviations from this distribution. By jointly training the classifier and the energy-based model, the system can effectively distinguish between normal and anomalous data points, improving the accuracy of anomaly detection.
Another application is data augmentation, where the estimated input data distribution from the energy-based model can be used to generate synthetic data points. By leveraging the learned distribution, the model can generate new data samples that are consistent with the underlying data distribution, enhancing the diversity of the training data and improving the model's robustness and generalization capabilities.
Additionally, the joint training approach can be applied to generative modeling tasks, such as image generation or text generation. By combining the discriminative capabilities of the classifier with the generative capabilities of the energy-based model, the system can learn to generate realistic and diverse samples that align with the input data distribution, leading to improved performance in generative modeling tasks.

Can the estimated input data distribution from the EBM be leveraged for other tasks, such as outlier detection or data augmentation, to further improve the pseudo-label learning process?

Yes, the estimated input data distribution from the energy-based model (EBM) can be leveraged for various tasks to enhance the pseudo-label learning process. One application is outlier detection, where the EBM can help identify data points that deviate significantly from the learned distribution. By flagging these outliers, the model can improve the quality of pseudo-label assignment by excluding anomalous data points that may introduce noise or bias into the training process.
Furthermore, the estimated input data distribution can be used for data augmentation by generating synthetic data points that align with the learned distribution. This augmented data can help increase the diversity of the training dataset, leading to better generalization and performance of the model. By incorporating augmented data samples that are consistent with the underlying data distribution, the model can learn more robust and accurate decision boundaries, improving its overall performance in pseudo-label learning tasks.