A Hybrid Generalized Zero-Shot Learning Method for Image Classification

 One of the main challenges in machine learning is the need for extensive training data, which increases the cost and time required for data preparation. In some cases, there may be limited data available, or privacy concerns may arise. Generalized zero-shot learning (GZSL) offers a suitable option in such situations, enabling the prediction of both seen and unseen classes. Although various methods have been proposed in this field, accurately recognizing seen and unseen classes still presents multiple challenges. This paper introduces a hybrid generalized zero-shot learning (H-GZSL) approach, which improved classification accuracy in GZSL. The proposed method simultaneously utilizes two classification techniques, enhancing classification accuracy for both seen and unseen classes. To address different scenarios and resolve conflicts in classification, a correction mechanism is employed. This mechanism compares the predicted classes from the two methods and assigns the instance to the most appropriate one. The proposed method was evaluated on the AWA2, SUN, aPY, CUB, and FLOW datasets. The evaluation separately measured the performance on seen and unseen classes, and the harmonic mean of classification accuracy across these classes was calculated. Compared to previous methods, the proposed approach achieved an improvement in the harmonic mean accuracy on the CUB and SUN datasets.