/Gradient-Centralization

A New Optimization Technique for Deep Neural Networks

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Gradient Centralization

Gradient Centralization: A New Optimization Technique for Deep Neural Networks

Introduction

  • Gradient Centralization (GC) is a simple and effective optimization technique for Deep Neural Networks (DNNs), which operates directly on gradients by centralizing the gradient vectors to have zero mean. It can both speedup training process and improve the final generalization performance of DNNs. GC is very simple to implement and can be easily embedded into existing gradient based DNN optimizers with only few lines of code. It can also be directly used to finetune the pre-trained DNNs.
Illustration of the GC operation on gradient matrix/tensor of weights in the fully-connected layer (left) and convolutional layer (right).
  • GC can be viewed as a projected gradient descent method with a constrained loss function. The Lipschitzness of the constrained loss function and its gradient is better so that the training process becomes more efficient and stable. Our experiments on various applications, including general image classification, fine-grained image classification, detection and segmentation and Person ReID demonstrate that GC can consistently improve the performance of DNN learning.
  • The optimizers are provided in the files: SGD.py, Adam.py and Adagrad.py, including SGD_GC, SGD_GCC, SGDW_GCC, Adam_GC, Adam_GCC, AdamW_GCC and Adagrad_GCC. The optimizers with "_GC" use GC for both Conv layers and FC layers, and the optimizers with "_GCC" use GC only for Conv layers. We can use the following codes to import SGD_GC:
from SGD import SGD_GC

Update

  • 2020/04/07:Release a pytorch implementation of optimizers with GC, and provide some examples on classification task, including general image classification (Mini-ImageNet, CIFAR100 and ImageNet) and Fine-grained image classification (FGVC Aircraft， Stanford Cars， Stanford Dogs and CUB-200-2011).

  • 2020/04/14:Release the code of GC on MMdetection and update some tables of experimental results.

Citation

@article{GradientCentra,
  title={Gradient-Centralization: A New Optimization Technique for Deep Neural Networks},
  author={Hongwei Yong and Jianqiang Huang and Xiansheng Hua and Lei Zhang},
  journal={Arxiv},
  year={2020}
}

Experiments

General Image Classification

  • Mini-ImageNet

The codes are in GC_code/Mini_ImageNet. The split dataset can be downloaded from here (Google drive) or here (Baidu drive, safe code: 1681). The following figure is training loss (left) and testing accuracy (right) curves vs. training epoch on the Mini-ImageNet. The ResNet50 is used as the DNN model. The compared optimization techniques include BN, BN+GC, BN+WS and BN+WS+GC.

  • CIFAR100

The codes are in GC_code/CIFAR100.

  • ImageNet

The codes are in GC_code/ImageNet. The following table is the Top-1 error rates on ImageNet w/o GC and w/ GC:

Backbone R50BN R50GN R101BN R101GN
w/o GC 23.71 24.50 22.37 23.34
w/ GC 23.21 23.53 21.82 22.14

The following figure is the training error (left) and validation error (right) curves vs. training epoch on ImageNet. The DNN model is ResNet50 with GN.

Fine-grained Image Classification

The codes are in GC_code/Fine-grained_classification. The preprocessed dataset can be downloaded from here. The following table is the testing accuracies on the four fine-grained image classification datasets with ResNet50:

Datesets FGVC Aircraft Stanford Cars Stanford Dogs CUB-200-2011
w/o GC 86.62 88.66 76.16 82.07
w/ GC 87.77 90.03 78.23 83.40

The following figure is the training accuracy (solid line) and testing accuracy (dotted line) curves vs. training epoch on four fine-grained image classification datasets:

Objection Detection and Segmentation

The codes are in MMdetection. Please let SGD.py in MMdetection\tools\, and update MMdetection\tools\train.py. Then if you want use SGD_GC optimizer, just update optimizer in the configs file. For example, if we want use SGD_GC to optimize Faster_RCNN with ResNet50 backbone and FPN, we update the 151th line in MMdetection/configs/faster_rcnn_r50_fpn_1x.py. The following table is the detection results on COCO by using Faster-RCNN and FPN with various backbone models:

Method Backbone AP AP.5 AP.75 Backbone AP AP.5 AP.75
w/o GC R50 36.4 58.4 39.1 X101-32x4d 40.1 62.0 43.8
w/ GC R50 37.0 59.0 40.2 X101-32x4d 40.7 62.7 43.9
w/o GC R101 38.5 60.3 41.6 X101-64x4d 41.3 63.3 45.2
w/ GC R101 38.9 60.8 42.2 X101-64x4d 41.6 63.8 45.4

The following table is the detection and segmentation results on COCO by using Mask-RCNN and FPN with various backbone models:

Method Backbone APb APb.5 APb.75 APm APm.5 APm.75
w/o GC R50 37.4 59.0 40.6 34.1 55.5 36.1
w/ GC R50 37.9 59.6 41.2 34.7 56.1 37.0
w/o GC R101 39.4 60.9 43.3 35.9 57.7 38.4
w/ GC R101 40.0 61.5 43.7 36.2 58.1 38.7
w/o GC X101-32x4d 41.1 62.8 45.0 37.1 59.4 39.8
w/ GC X101-32x4d 41.6 63.1 45.5 37.4 59.8 39.9
w/o GC X101-64x4d 42.1 63.8 46.3 38.0 60.6 40.9
w/ GC X101-64x4d 42.8 64.5 46.8 38.4 61.0 41.1
w/o GC R50 (4c1f) 37.5 58.2 41.0 33.9 55.0 36.1
w/ GC R50 (4c1f) 38.4 59.5 41.8 34.6 55.9 36.7
w/o GC R101GN 41.1 61.7 44.9 36.9 58.7 39.3
w/ GC R101GN 41.7 62.3 45.3 37.4 59.3 40.3
w/o GC R50GN+WS 40.0 60.7 43.6 36.1 57.8 38.6
w/ GC R50GN+WS 40.6 61.3 43.9 36.6 58.2 39.1

Person ReId

The codes are in GC_code/PersonReId.