Joint Application of Transfer Learning and Generative Adversarial Networks in Addressing the Long-Tail Problem

In recent years, the deep learning, and artificial intelligence, have advanced rapidly and make significant breakthroughs with the help of generative architectures. In general, these are broadly broken down into either Text to image (T2I) architectures or Large Language Models (LLMs). Generating AI has been broadly explored in most domains, but the detection of long tailed is undeveloped. The scarcity of data for tail classes becomes the key challenge among its shortage in long-tailed image classification, and thus poor generalization tends to occur. To address this issue, traditional methods make use of constructive learning, mix based augmentation, re weighting, and resampling. But still, all these techniques do not generalize well to the representations of tail classes for data imbalance. In order to tackle the limitations of it, this research develops a hybrid generative learning model with synthetic data augmentation, contrastive learning, and adaptive label correction for improving long tailed image detection. The architecture takes advantage of T2I models to create synthetic data that is then refined via mix-based augmentation (MixUP and CutMix) and processed through Contrastive learning base encoder. The meaningful representations are extracted using a ResNet backbone and the model generalization is improved using supervised contrastive learning (SCL). Moreover, there is a label correction module which dynamically identifies low quality synthetic labels using KNN method, providing data integrity. The proposed method is also demonstrated to significantly improve the classification accuracy for the tail classes, and to outperform conventional techniques. Generative AI is shown to promise to fill the gap between synthetically and real-life data and make robust long tailed image classification possible with an adaptive and correcting nature.