In MDA, the goal is to leverage the information from all available source domains to improve the performance of the model on the target domain. We are using ensemble method Votingon 3 dataset for classification on a separate 4th dataset
Introduction
Multi-source domain adaptation (MDA) is a fascinating area in machine learning that aims to transfer knowledge from multiple source domains to an unlabeled target domain.
In MDA, the goal is to leverage the information from all available source domains to improve the performance of the model on the target domain.
Ensemble
Ensemble methods work because they combine the strengths of multiple models to produce a more robust and accurate prediction or decision than any single model could achieve on its own
Source-Specific Models
Source-Specific Models: Train individual models for each source domain and combine their predictions for the target domain using ensemble techniques like weighted averaging or majority voting
Conclusion
It looks like we're getting low accuracy when testing our 4th dataset (QuickDraw), and underfitting is occurring during the training loop.Here are a few options we can implement next to increase our results.
Increase Model Complexity Enhance the capability of your model to capture complex patterns by increasing its depth and the number of filters.
Data Augmentation Use more diverse data augmentation techniques to enrich the training data.
Fine-Tune on Specific Data Fine-tune your ensemble on a portion of the QuickDraw dataset to help it adapt better.
Hyperparameter Tuning Experiment with different learning rates, batch sizes, and optimizers to find the optimal configuration.
Regularization Techniques Add dropout or L2 regularization to prevent overfitting and improve model robustness.