Different learning techniques have different applications. I do not believe those techniques are applicable to the hypothetical use-cases of this dataset.
Perhaps semi-supervised could be utilized, which reduces the required annotation by some factor k, but still leaves it as a function of the dataset.
Self-supervised basically replaces human annotation with machine annotation, making it only applicable to a small subset of tasks in which this is possible (e.g. you could train "guess time from picture" using EXIF timestamp).
Unsupervised is only applicable to very specific tasks.
That's not true. There is a large and growing body of research on semi-supervised, self-supervised, and unsupervised learning that can take advantage of these unlabelled images.