When working on unsupervised domain adaptation techniques, such as super resolution, choosing the best model can be a challenge, especially when you have limited target data without ground truth. This is because you can’t validate your model using the usual method of comparing it to labeled target data.
The Problem
In traditional machine learning, you’d train a model, validate it on a separate dataset, and save the best-performing model. But what if you don’t have that luxury?
A Common Dilemma
I recently came across a Reddit post from someone in a similar predicament. They were working on super resolution using unsupervised domain adaptation techniques and had plenty of paired source data but very little target data without ground truth. They were struggling to find a way to save the best model during training.
Transfer Score: A Solution for Classification Tasks
One possible solution is to use a transfer score, which is a metric that doesn’t require target labels. However, this approach is specifically designed for classification tasks, not super resolution.
So, What Can You Do?
While there isn’t a straightforward answer, here are some potential strategies to consider:
- Use alternative evaluation metrics: Instead of relying on traditional metrics like PSNR or SSIM, explore other metrics that don’t require ground truth, such as no-reference image quality assessment metrics.
- Implement self-supervised learning techniques: These techniques can help your model learn from the target data itself, without the need for labels.
- Leverage generative adversarial networks (GANs): GANs can be used for unsupervised learning and may provide a way to evaluate your model without target labels.
The Takeaway
Choosing the best model in unsupervised domain adaptation is an open problem, and there isn’t a one-size-fits-all solution. However, by exploring alternative evaluation metrics, self-supervised learning techniques, and GANs, you may be able to find a way to tackle this challenge in your own project.
*Further reading: Unsupervised Domain Adaptation*