Hard Whitening Approach

Hard whitening, a technique for normalizing and decorrelating data features, is being investigated for its impact on various machine learning tasks. Current research focuses on its application in self-supervised learning, where it aims to improve feature quality and prevent undesirable "collapse" phenomena, often employing ZCA whitening or variations thereof. While studies show benefits in some contexts, like self-supervised visual representation learning, its effectiveness is model and task-dependent, with negative impacts observed in certain natural language processing applications. This ongoing research seeks to refine understanding of whitening's effects and optimize its use for improved model performance and interpretability.