The self-consistency mechanism in the ADTRS model, while effective in enhancing robustness, has certain limitations. One primary limitation is its reliance on horizontal flipping as the sole equivariant transformation. This may not capture the full range of transformations that can occur in real-world scenarios, such as rotations, scaling, or perspective changes, which can significantly affect image quality assessments.
To extend the self-consistency mechanism, the model could be adapted to incorporate a wider variety of transformations. For instance, implementing a set of transformations that includes random rotations, scaling, and color jittering could provide a more comprehensive self-supervisory signal. This would allow the model to learn invariance to a broader range of distortions, improving its robustness.
Moreover, utilizing a learned transformation network that can dynamically generate transformations based on the input image characteristics could enhance the model's adaptability. This network could identify the most relevant transformations for each image, ensuring that the self-consistency loss is applied in a context-sensitive manner.
Additionally, incorporating a multi-view approach, where multiple augmented versions of the same image are processed simultaneously, could further strengthen the self-consistency mechanism. This would allow the model to learn from various perspectives of the same content, enhancing its ability to generalize across different distortions.