Image Classification Based on Few-Shot Learning
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This blog classifies and summarizes the current image classification algorithms based on Few-shot learning. According to the modeling methods of different data types, Few-shot image classification algorithms are divided into convolution neural network model and graph neural network model. Convolution neural network model is divided into four learning paradigms: transfer learning, meta learning, Bayesian learning and dual learning. Two kinds of algorithms are introduced in detail. Finally, for the current Few-shot image classification algorithm, we discuss the ethical oriented implications in terms of interpretability and privacy.