The expected outcomes of this research include: 1) A regularization method based on cognitive dissonance theory that can effectively mitigate performance degradation in deep learning models caused by data inconsistency or noise during training. 2) Experimental validation demonstrating the method's versatility and efficiency in fields such as natural language processing and computer vision, particularly in terms of model robustness and generalization ability. 3) A new theoretical framework and technical tool for the regularization of deep learning models, advancing related technologies. 4) New application scenarios and optimization ideas for OpenAI’s models and systems, particularly in handling complex data inconsistency issues. These outcomes will enhance the robustness of OpenAI models in complex data environments, promoting their applications in more fields.

Cognitive Dissonance

Exploring deep learning through cognitive dissonance regularization framework development.