摘要： In self-supervised learning, an AI technique where the training data is automatically labeled by a feature extractor, the said extractor not uncommonly exploits low-level features (known as “shortcuts”) that cause it to ignore useful representations. In search of a technique that might help to remove those shortcuts autonomously, researchers at Google Brain developed a framework — a “lens” — that makes changes enabling self-supervised models to outperform those trained in a conventional fashion.