Distribution Image

Distribution image research centers on improving the robustness of machine learning models, particularly deep learning models like convolutional neural networks and generative adversarial networks (GANs), to handle images outside their training data distribution (out-of-distribution or OOD images). Current research focuses on developing effective OOD detection methods using techniques like Mahalanobis distance, k-nearest neighbors, diffusion model reconstruction errors, and heatmap analysis, often incorporating dimensionality reduction for efficiency. This work is crucial for deploying reliable AI systems in real-world applications, such as medical image analysis and remote sensing, where encountering OOD images is inevitable and misclassification can have serious consequences.