Could the guided diffusion-based data augmentation approach be further enhanced by incorporating additional domain-specific knowledge or constraints to generate more realistic and diverse synthetic samples
Enhancing the guided diffusion-based data augmentation approach with additional domain-specific knowledge or constraints can lead to more realistic and diverse synthetic samples:
Semantic Constraints: Incorporating semantic constraints specific to the text or audio domain can improve the quality of synthetic samples. For text, constraints related to grammar, semantics, or topic coherence can be integrated. For audio, constraints on pitch, tempo, or sound characteristics can be applied.
Domain-Specific Embeddings: Utilizing domain-specific embeddings or representations can enhance the guided diffusion process. For text, pre-trained language models like BERT or GPT can guide the augmentation. For audio, domain-specific feature representations can guide the generation of diverse samples.
Task-Specific Guidance: Tailoring the augmentation process based on the specific task requirements can improve the relevance and diversity of synthetic samples. Task-specific constraints or objectives can guide the diffusion model to generate samples that align with the task at hand.
Feedback Mechanisms: Implementing feedback mechanisms that incorporate domain experts' input or user feedback can refine the augmentation process. Iterative refinement based on feedback can ensure that the generated samples meet domain-specific criteria and quality standards.
Hybrid Approaches: Combining guided diffusion with domain-specific generative models or techniques can further enhance the diversity and realism of synthetic samples. Hybrid approaches that leverage the strengths of different methods can lead to more effective data augmentation in text and audio domains.