Evaluating Sparse Autoencoders: From Shallow Design to Matching Pursuit

A recent study published on arXiv evaluates sparse autoencoders (SAEs) using the MNIST dataset to better understand their performance in a controlled environment. The research focuses on shallow architectures of SAEs, which rely heavily on a quasi-orthogonality assumption. This assumption is identified as a key dependency that may limit the models' capacity to extract meaningful features from neural representations. The study highlights that such reliance on quasi-orthogonality restricts the feature extraction process, potentially hindering the effectiveness of shallow sparse autoencoders. By examining these limitations, the paper contributes to ongoing discussions about the design and capabilities of sparse autoencoders in machine learning. This evaluation provides valuable insights into how architectural choices impact the ability of SAEs to learn useful representations. The findings underscore the need for reconsidering assumptions in shallow SAE designs to improve their feature extraction capabilities.