Generalized few-shot classification has become an active research area in deep learning, aiming to enable models to recognize novel classes with very limited labeled samples while maintaining performance on base classes seen during training. Unlike standard few-shot learning, which focuses solely on unseen classes, generalized few-shot classification (GFSL) addresses the more realistic challenge of jointly classifying both base and novel classes, often leading to strong class imbalance issues. Research in this field explores meta-learning approaches such as prototypical networks, model-agnostic meta-learning (MAML), and relation networks, as well as metric-learning frameworks that learn transferable embeddings capable of discriminating across heterogeneous tasks. Recent works further incorporate attention mechanisms, data augmentation, generative models, and transductive inference strategies to mitigate bias toward base classes and improve generalization to novel categories. GFSL has been successfully applied in computer vision, natural language processing, and medical imaging, demonstrating its potential for real-world scenarios where large-scale labeled data is scarce but adaptability to new concepts is crucial.