Shot Classifier

Shot classifiers are machine learning models designed to perform image classification tasks with extremely limited training data (few-shot learning). Current research focuses on improving the robustness and accuracy of these classifiers, particularly addressing issues like spurious bias and sensitivity to the number of training examples, often employing architectures based on graph neural networks, vision-language models (like CLIP), and meta-learning techniques. These advancements are significant because they enable the application of deep learning to scenarios with scarce labeled data, impacting fields like medical image analysis and other areas where obtaining large annotated datasets is costly or impractical.