Papers › Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation

Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation

1 May 2022International Journal of Advanced Research in Engineering and Technology (IJARET) 2022 5archive 2025-07-28

Allena Venkata Sai Abhishek, Dr. Venkateswara Rao Gurrala

The data in the real world consists of various kinds of painful features. A majorly found one is the class imbalance in which the number of examples in different classes in a dataset is unequal. The class imbalance is being resolved using various sampling techniques on the data. Augmentation technique Augmentation is one of the essential steps in any machine learning pipeline and is used for oversampling the data of minority classes. This paper aims to improve the model performance is being enhanced with removing the class imbalance problem by using various Augmentation approaches to generate various balanced augmented datasets using various data augmentation techniques & random sampling. The accuracies are acquired for each augmentation technique using a RESNET18 model. The model is run up to 100 epochs for each case, and the best accuracies are compared. This iterative comparison of various augmentation techniques has shown stunning insights into the effectiveness of multiple datasets.

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Tasks

Classification Data Augmentation Data Visualization Detecting Image Manipulation Image Augmentation Image Classification Image Cropping Image Denoising Image Generation Image Manipulation Image Manipulation Detection Image Matting Image Morphing Image Stitching Image-Variation ROI-based image generation image smoothing

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Image Classification	Deep PCB	ResNet	Accuracy (%)	97.5	#1 of 1	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block Convolution Global Average Pooling Image Scale Augmentation Kaiming Initialization Mask R-CNN Max Pooling RPN ReLU Residual Block Residual Connection RoIAlign Softmax

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