Maximizing Generalization: The Effect of Different Augmentation Techniques on Lightweight Vision Transformer for Bengali Character Classification

• URL: http://arxiv.org/abs/2603.02591v1
• Date: Tue, 03 Mar 2026 04:14:11 GMT
• Title: Maximizing Generalization: The Effect of Different Augmentation Techniques on Lightweight Vision Transformer for Bengali Character Classification
• Authors: Rafi Hassan Chowdhury, Naimul Haque, Kaniz Fatiha,
• Abstract summary: Deep learning models rely heavily on large datasets to avoid overfitting.<n>Large-scale datasets may not be available in many domains, particularly for resource-limited languages such as Bengali.<n>Data augmentation refers to a set of techniques applied to data to increase its size and diversity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning models have proven to be highly effective in computer vision, with deep convolutional neural networks achieving impressive results across various computer vision tasks. However, these models rely heavily on large datasets to avoid overfitting. When a model learns features with either low or high variance, it can lead to underfitting or overfitting on the training data. Unfortunately, large-scale datasets may not be available in many domains, particularly for resource-limited languages such as Bengali. In this experiment, a series of tests were conducted in the field of image data augmentation as an approach to addressing the limited data problem for Bengali handwritten characters. The study also provides an in-depth analysis of the performance of different augmentation techniques. Data augmentation refers to a set of techniques applied to data to increase its size and diversity, making it more suitable for training deep learning models. The image augmentation techniques evaluated in this study include CLAHE, Random Rotation, Random Affine, Color Jitter, and their combinations. The study further explores the use of augmentation methods with a lightweight model such as EfficientViT. Among the different augmentation strategies, the combination of Random Affine and Color Jitter produced the best accuracy on the Ekush [1] and AIBangla [2] datasets, achieving accuracies of 97.48% and 97.57%, respectively. This combination outperformed all other individual and combined augmentation techniques. Overall, this analysis presents a thorough examination of the impact of image data augmentation in resource-scarce languages, particularly in the context of Bengali handwritten character recognition using lightweight models.

Related papers

• Enhancing Image Classification with Augmentation: Data Augmentation Techniques for Improved Image Classification [0.0]
  Convolutional Neural Networks (CNNs) serve as the workhorse of deep learning, finding applications in various fields that rely on images.<n>In this study, we explore the effectiveness of 11 different sets of data augmentation techniques, which include three novel sets proposed in this work.<n>The ensemble of image augmentation techniques proposed emerges as the most effective on the Caltech-101 dataset.
  arXiv  Detail & Related papers  (2025-02-25T23:03:30Z)
• Image compositing is all you need for data augmentation [6.647179199462945]
  This paper investigates the impact of various data augmentation techniques on the performance of object detection models.<n>We fine-tune the model on a custom dataset consisting of commercial and military aircraft, applying different augmentation strategies.
  arXiv  Detail & Related papers  (2025-02-19T18:24:02Z)
• A Simple Background Augmentation Method for Object Detection with Diffusion Model [53.32935683257045]
  In computer vision, it is well-known that a lack of data diversity will impair model performance. We propose a simple yet effective data augmentation approach by leveraging advancements in generative models. Background augmentation, in particular, significantly improves the models' robustness and generalization capabilities.
  arXiv  Detail & Related papers  (2024-08-01T07:40:00Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification [57.1795052451257]
  We study the dependence of the GAN-based augmentation performance on dataset size with a focus on small samples. We train StyleGAN2-ADA with both sets and then, after validating the quality of generated images, we use trained GANs as one of the augmentations approaches in multi-class classification problems. The GAN-based augmentation approach is found to be comparable with classical augmentation in the case of medium and large datasets but underperforms in the case of smaller datasets.
  arXiv  Detail & Related papers  (2024-01-26T08:28:13Z)
• StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data [129.92449761766025]
  We propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models. Our research includes comprehensive experiments conducted on various datasets.
  arXiv  Detail & Related papers  (2023-08-20T12:43:52Z)
• Domain Generalization for Mammographic Image Analysis with Contrastive Learning [62.25104935889111]
  The training of an efficacious deep learning model requires large data with diverse styles and qualities. A novel contrastive learning is developed to equip the deep learning models with better style generalization capability. The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets.
  arXiv  Detail & Related papers  (2023-04-20T11:40:21Z)
• Exploring the Effects of Data Augmentation for Drivable Area Segmentation [0.0]
  We focus on investigating the benefits of data augmentation by analyzing pre-existing image datasets. Our results show that the performance and robustness of existing state of the art (or SOTA) models can be increased dramatically.
  arXiv  Detail & Related papers  (2022-08-06T03:39:37Z)
• An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs [67.23285413610243]
  Self-supervision based on the information extracted from large knowledge graphs has been shown to improve the generalization of language models. We study the effect of knowledge sampling strategies and sizes that can be used to generate synthetic data for adapting language models.
  arXiv  Detail & Related papers  (2022-05-21T19:49:04Z)
• Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-Case [0.0]
  This paper challenges small and imbalanced datasets based on the example of a plant phenomics domain. We introduce an image augmentation framework, which enables us to extremely enlarge the number of training samples. We prove that our augmentation method increases model performance when only a few training samples are available.
  arXiv  Detail & Related papers  (2021-02-24T14:08:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.