Data-driven machine learning approaches are being increasingly used to solve partial differential equations (PDEs).

Sparse representation plays a critical role in image restoration problems, such as image superresolution[1,2,3],denoising[4],compression artifactsremoval[5],andmanyothers[6,7].

Throughout this work, we investigate if diversity across neural activations ( 2) reflects model generalizability.