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Optimizers that further adjust the scale of gradient, such as Adam, NaturalGradient (NG), etc., despite widely concerned and used by the community, areoften found poor generalization performance, compared with Stochastic GradientDescent (SGD). They tend to converge excellently at the beginning of trainingbut are weak at the end. An immediate idea is to complement the strengths ofthese algorithms with SGD. However, a truncated replacement of optimizer oftenleads to a crash of the update pattern, and new algorithms often require manyiterations to stabilize their search direction. Driven by this idea and toaddress this problem, we design and present a regularized natural gradientoptimization algorithm with look-ahead strategy, named asymptotic naturalgradient (ANG). According to the total iteration step, ANG dynamic assembles NGand Euclidean gradient, and updates parameters along the new direction usingthe intensity of NG. Validation experiments on CIFAR10 and CIFAR100 data setsshow that ANG can update smoothly and stably at the second-order speed, andachieve better generalization performance.

AsymptoticNG: A regularized natural gradient optimization algorithm with look-ahead strategy