To enhance the label-guided sample generation method for better mimicking the real data distribution and improving unlearning performance, several strategies can be employed:
Adversarial Training: Incorporating adversarial training techniques can help the sample generator produce more realistic and diverse synthetic samples that closely resemble the distribution of real data. Adversarial training can encourage the generator to generate samples that are indistinguishable from real data, enhancing the quality of the synthetic forget set.
Data Augmentation Techniques: Leveraging data augmentation methods such as rotation, translation, and scaling can introduce variability into the generated samples, making them more representative of the underlying data distribution. By augmenting the synthetic samples with realistic transformations, the generator can better capture the nuances of the real data distribution.
Domain Adaptation: Implementing domain adaptation techniques can enable the sample generator to learn domain-specific features and characteristics, aligning the synthetic samples more closely with the distribution of real data. By adapting the generator to the target domain of business documents, the generated samples can better reflect the complexities and nuances present in the document data.
Feedback Mechanisms: Introducing feedback mechanisms that evaluate the quality and relevance of the generated samples can help iteratively improve the sample generation process. By incorporating feedback from the unlearning process or model performance, the generator can adjust its sampling strategy to produce more effective synthetic samples for unlearning.
By integrating these strategies into the label-guided sample generation method, the quality, diversity, and realism of the synthetic samples can be enhanced, leading to improved mimicry of the real data distribution and ultimately enhancing the performance of machine unlearning for document classification tasks.