Eugene Yan's article tackles the common problem of building labels from scratch for machine learning. It lays out four techniques: semi-supervised learning, active learning, weakly supervised learning, and human-in-the-loop review.
Each method has a distinct role. Semi-supervised learning expands from a small set of seed labels, active learning decides which examples need human attention, weak supervision creates labels via rules and heuristics, and the human loop verifies and corrects the results. The article treats these as complementary pieces of a single bootstrapping pipeline rather than competing options.
Because there is only one source, there is no differing viewpoint to note; the author's framework is presented as a coherent whole.