Enhancing Deep Learning Models for Image Recognition with Transfer Learning and Augmented Data

Abstract

In the realm of computer vision, deep learning models have exhibited remarkable capabilities in image recognition tasks. However, achieving high accuracy and robustness often necessitates vast labeled datasets and substantial computational resources. To address these challenges, this research paper delves into the utilization of transfer learning and augmented data to enhance the performance of deep learning models for image recognition.

Transfer learning enables the extraction of valuable knowledge from pre-trained models, effectively reducing the demand for extensive labeled data. Augmented data techniques involve the synthesis of additional training examples, further enriching the model's learning process. This paper investigates the integration of both strategies, seeking to maximize the advantages of each.

We present a comprehensive analysis of transfer learning methods, including fine-tuning and feature extraction, along with a detailed exploration of data augmentation techniques such as rotation, flipping, and color jittering. Through empirical experiments, we demonstrate the impact of these strategies on image recognition tasks across various datasets and network architectures.

Our results show significant improvements in classification accuracy, generalization, and robustness, even when limited labeled data is available. We also discuss the trade-offs and challenges associated with implementing these methods in real-world applications.

This research contributes to the ongoing efforts to make deep learning models more accessible and effective for image recognition tasks, paving the way for their widespread deployment in diverse domains.