Mini ImageNet

Mini-ImageNet is a benchmark dataset commonly used to evaluate few-shot learning (FSL) algorithms, which aim to classify images with limited training examples. Current research focuses on improving FSL performance on Mini-ImageNet by addressing challenges like catastrophic forgetting (in class-incremental learning) and overfitting, employing techniques such as contrastive learning, prompt tuning, and adaptive similarity metrics within various model architectures including transformers and prototypical networks. These advancements contribute to a broader understanding of FSL and have implications for real-world applications where labeled data is scarce, such as in medical image analysis and object recognition in low-resource settings.