To improve the balance between classification and localization performance for histology images using SFDA methods, several strategies can be implemented:
Improved Pseudo-Labeling: Enhancing the accuracy of pseudo-labels generated during the adaptation process is crucial. Utilizing more advanced clustering algorithms or incorporating uncertainty estimation techniques can help in obtaining more reliable pseudo-labels.
Multi-Task Learning: Implementing a multi-task learning approach where the model is trained to optimize both classification and localization objectives simultaneously can help in achieving a better balance between the two tasks.
Regularization Techniques: Incorporating regularization techniques that penalize the model for focusing too much on one task over the other can help in encouraging a more balanced optimization strategy.
Task-Specific Adaptation: Tailoring the adaptation process to prioritize either classification or localization based on the specific requirements of the histology dataset can help in achieving a more optimal balance between the two tasks.
Fine-Tuning Strategies: Experimenting with different fine-tuning strategies, such as adjusting the learning rate schedules or weight decay parameters, can also help in improving the overall performance of SFDA methods for histology images.