  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.
