Before revision:  We show that AdaHessian achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods, including variants of ADAM . In particular, we perform extensive tests on CV, NLP, and recommendation system tasks and find that AdaHessian : (i) achieves 1.80  %DIF < /1.45\\% higher accuracy on ResNets20/32 on Cifar10, and 5.55\\% higher accuracy on ImageNet as compared to ADAM;
After revision:  We show that AdaHessian achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods, including variants of ADAM . In particular, we perform extensive tests on CV, NLP, and recommendation system tasks and find that ADAHESSIAN : (i) achieves 1.80  %DIF < /1.45\\% higher accuracy on ResNets20/32 on Cifar10, and 5.55\\% higher accuracy on ImageNet as compared to ADAM;