Distribution Consistency

Distribution consistency in machine learning focuses on aligning the data distributions between different subsets, such as labeled and unlabeled data or different views of the same data, to improve model performance and generalization. Current research emphasizes developing self-training methods and novel loss functions that explicitly address distribution discrepancies, often within graph neural network (GNN) frameworks or generative models, by incorporating techniques like homophily-aware selection strategies and directional distribution consistency losses. This research is significant because it tackles the challenges of limited labeled data and potential biases introduced during model training, leading to more robust and accurate models across various applications, including node classification and few-shot learning.