To extend the Approx-FIRAL algorithm for handling non-i.i.d. data distributions in the unlabeled pool, several strategies can be employed. First, the algorithm could incorporate a stratified sampling approach during the selection of the initial labeled set (X_o) and the unlabeled pool (X_u). By ensuring that the unlabeled pool reflects the underlying distribution of the data, the algorithm can better capture the diversity of classes, especially in imbalanced scenarios.
Second, the use of a modified Fisher Information Ratio that accounts for the distribution of the data can be beneficial. This could involve weighting the contributions of different classes based on their prevalence in the unlabeled pool, thus allowing the algorithm to focus on underrepresented classes during the active learning process.
Additionally, integrating techniques such as domain adaptation or transfer learning could enhance the algorithm's robustness to non-i.i.d. distributions. By leveraging pre-trained models on similar tasks or datasets, Approx-FIRAL can better generalize to the specific characteristics of the non-i.i.d. data.
Finally, the algorithm could be adapted to include uncertainty sampling methods that prioritize samples with high uncertainty, which is particularly useful in non-i.i.d. settings where certain classes may be more challenging to classify. This would ensure that the model is trained on the most informative samples, improving overall performance.