Enhancing Image Classification Using Few-Shot Learning Prototypical Networks with ResNet-18: Detection, Accuracy Enhancement, and Optimization

Dr. S. M. Kulkarni, S. S. Pawar, A. A. Dekhane, S. L. Suryawanshi
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Abstract

Image classification, especially in scenarios with limited data, presents significant challenges. Few shot learning (FSL) aims to address these challenges by training models that can generalize from a few examples. This paper explores the integration of prototypical networks with ResNet-18 for feature extraction to enhance image classification accuracy. Prototypical networks are designed to create a prototype representation for each class, which can then be used to classify new examples based on their distance to these prototypes. By leveraging ResNet-18's powerful feature extraction capabilities, we aim to improve the quality of these prototypes, thereby enhancing classification performance.We propose various methods for accuracy enhancement and optimization, including hyperparameter tuning, regularization techniques, and advanced methods like attention mechanisms and metric learning. Hyperparameter tuning involves adjusting the model's parameters to find the optimal settings that yield the best performance. Regularization techniques, such as dropout and weight decay, help prevent overfitting and improve the model's generalization capabilities. Advanced methods like attention mechanisms can focus on the most relevant parts of the image, while metric learning aims to learn a distance metric that better reflects the similarities between images.Our experiments on datasets like Mini-ImageNet and Omniglot demonstrate significant improvements in classification performance. These datasets are commonly used benchmarks in the few-shot learning community, allowing us to compare our results with existing methods. The integration of prototypical networks with ResNet-18, along with the proposed optimization techniques, provides a robust approach for tackling the challenges of image classification in few-shot learning scenarios. Key Words: Few-shot learning, ResNet-18, Prototypical Networks.
利用 ResNet-18 的少量学习原型网络加强图像分类:检测、准确度提升和优化
图像分类,尤其是在数据有限的情况下,面临着巨大的挑战。Few shot learning(FSL)旨在通过训练能从少量示例中概化的模型来应对这些挑战。本文探讨了原型网络与 ResNet-18 在特征提取方面的集成,以提高图像分类的准确性。原型网络旨在为每个类别创建一个原型表示,然后根据新示例与这些原型的距离对其进行分类。通过利用 ResNet-18 强大的特征提取功能,我们旨在改善这些原型的质量,从而提高分类性能。我们提出了各种提高和优化准确性的方法,包括超参数调整、正则化技术以及注意力机制和度量学习等高级方法。超参数调整包括调整模型参数,以找到产生最佳性能的最优设置。正则化技术,如剔除和权重衰减,有助于防止过度拟合,提高模型的泛化能力。我们在 Mini-ImageNet 和 Omniglot 等数据集上的实验表明,分类性能有了显著提高。这些数据集是少量图像学习领域常用的基准,因此我们可以将我们的结果与现有方法进行比较。原型网络与 ResNet-18 的整合,以及所提出的优化技术,为应对少量学习场景中的图像分类挑战提供了一种稳健的方法。关键字少量学习 ResNet-18 原型网络
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