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Automatic image caption generation is a challenging AI problem since it requires utilization of several techniques from different computer science domains such as computer vision and natural language processing. Deep learning techniques have demonstrated outstanding results in many different applications.However, data augmentation in deep learning, which replicates the amount and the variety of training data available for learningmodels without the burden of collecting new data, is a promising field in machine learning. Generating textual description for agiven image is a challenging task for computers. Nowadays, deep learning performs a significant role in the manipulationof visual data with the help of Convolutional Neural Networks (CNN). In this study, CNNs are employed to train predictionmodels which will help in automatic image caption generation. The proposed method utilizes the concept of data augmentationto overcome the fuzziness of well-known image caption generation models. Flickr8k dataset is used in the experimental work of thisstudy and the BLEU score is applied to evaluate the reliability of the proposed method. The results clearly show the stabilityof the outcomes generated through the proposed method when compared to others.

Data Augmentation to Stabilize Image Caption Generation Models in Deep Learning