AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods
This addresses a critical convergence issue in widely used optimization algorithms like Adam, benefiting machine learning practitioners, but it is incremental as it builds on existing adaptive methods.
The paper tackles the non-convergence problem of Adam and other adaptive learning rate methods by identifying an inappropriate correlation between gradient and second-moment terms, which causes biased step sizes. It proposes AdaShift, a method that decorrelates these terms using temporal shifting, solving the non-convergence issue while maintaining competitive performance with Adam in training speed and generalization.
Adam is shown not being able to converge to the optimal solution in certain cases. Researchers recently propose several algorithms to avoid the issue of non-convergence of Adam, but their efficiency turns out to be unsatisfactory in practice. In this paper, we provide new insight into the non-convergence issue of Adam as well as other adaptive learning rate methods. We argue that there exists an inappropriate correlation between gradient $g_t$ and the second-moment term $v_t$ in Adam ($t$ is the timestep), which results in that a large gradient is likely to have small step size while a small gradient may have a large step size. We demonstrate that such biased step sizes are the fundamental cause of non-convergence of Adam, and we further prove that decorrelating $v_t$ and $g_t$ will lead to unbiased step size for each gradient, thus solving the non-convergence problem of Adam. Finally, we propose AdaShift, a novel adaptive learning rate method that decorrelates $v_t$ and $g_t$ by temporal shifting, i.e., using temporally shifted gradient $g_{t-n}$ to calculate $v_t$. The experiment results demonstrate that AdaShift is able to address the non-convergence issue of Adam, while still maintaining a competitive performance with Adam in terms of both training speed and generalization.