Transformation Behavior of Images in Latent Space

The paper investigates the transformation behavior of images in latent space for histopathology classification, focusing on encoder networks from Lunit Inc., Bioptimus, and the Meta Research Team. It finds that while embeddings of original and transformed images maintain proximity, indicating robustness, they are not entirely invariant to transformations, highlighting the need for tailored encoder training to enhance performance in downstream tasks. This research underscores the importance of understanding latent space behavior for improving data augmentation strategies in histopathological applications.