Perturbated Gradients Updating within Unit Space for Deep Learning
This work addresses optimization challenges in image classification, offering a novel method that improves accuracy on standard benchmarks.
The paper tackles optimization in deep learning by proposing a new optimizer called Perturbated Unit Gradient Descent (PUGD), which achieves state-of-the-art Top-1 accuracy on Tiny ImageNet and competitive results on CIFAR-10 and CIFAR-100.
In deep learning, optimization plays a vital role. By focusing on image classification, this work investigates the pros and cons of the widely used optimizers, and proposes a new optimizer: Perturbated Unit Gradient Descent (PUGD) algorithm with extending normalized gradient operation in tensor within perturbation to update in unit space. Via a set of experiments and analyses, we show that PUGD is locally bounded updating, which means the updating from time to time is controlled. On the other hand, PUGD can push models to a flat minimum, where the error remains approximately constant, not only because of the nature of avoiding stationary points in gradient normalization but also by scanning sharpness in the unit ball. From a series of rigorous experiments, PUGD helps models to gain a state-of-the-art Top-1 accuracy in Tiny ImageNet and competitive performances in CIFAR- {10, 100}. We open-source our code at link: https://github.com/hanktseng131415go/PUGD.