Cyclical Log Annealing as a Learning Rate Scheduler
This is an incremental improvement for machine learning practitioners, as it offers a new scheduler variant with potential for use in online convex optimization.
The paper tackles the problem of learning rate scheduling by introducing cyclical log annealing, a method that aggressively restarts step sizes during training. The result is that it performed similarly to cosine annealing on CIFAR-10 with transformer-enhanced residual networks, though no concrete numbers are provided.
A learning rate scheduler is a predefined set of instructions for varying search stepsizes during model training processes. This paper introduces a new logarithmic method using harsh restarting of step sizes through stochastic gradient descent. Cyclical log annealing implements the restart pattern more aggressively to maybe allow the usage of more greedy algorithms on the online convex optimization framework. The algorithm was tested on the CIFAR-10 image datasets, and seemed to perform analogously with cosine annealing on large transformer-enhanced residual neural networks. Future experiments would involve testing the scheduler in generative adversarial networks and finding the best parameters for the scheduler with more experiments.