Perturbated Gradients Updating within Unit Space for Deep Learning
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.
PDF Abstract
Datasets
CIFAR-10
ImageNet
CIFAR-100
Tiny ImageNet
Results from the Paper
Ranked #5 on
Image Classification
on Tiny ImageNet Classification
(using extra training data)
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Uses Extra Training Data |
Benchmark |
|---|---|---|---|---|---|---|---|
| Image Classification | CIFAR-10 | ViT-B/16 (PUGD) | Percentage correct | 99.13 | # 12 | ||
| Image Classification | CIFAR-100 | ViT-B/16 (PUGD) | Percentage correct | 93.95 | # 6 | ||
| Image Classification | Tiny ImageNet Classification | DeiT-B/16 (PUGD) | Validation Acc | 91.02% | # 5 | ||
| Image Classification | Tiny ImageNet Classification | ViT-B/16 (PUGD) | Validation Acc | 90.74% | # 7 |
