Discovering shared interpretable operations in image compression autoencoders
The study investigates if deep learning image compression autoencoders use universal, shared operations across different images. By analyzing internal Jacobian matrices, researchers found that certain filters are highly correlated across images, suggesting a set of transferable compression behaviors. This allows for designing simpler, low-complexity models inspired by complex deep learning architectures.