IDN is a challenge in machine learning, where the accuracy of a label can depend on the specific content of an image, making some labels inherently more prone to errors due to ambiguous or insufficient visual information.

• Innovative Graphical Modelling for Enhanced Label Accuracy: NoisyLabels introduces a groundbreaking graphical modeling approach, combining generative and discriminative models to effectively manage instance-dependent noise.
• Superior Performance with Diverse Data Sets: Demonstrating exceptional results across various IDN benchmarks, including synthetic and real-world datasets.
• State-of-the-Art Results for Unseen Data Categories: By achieving remarkable accuracy on unseen classes, InstanceGM provides businesses with a robust tool for adapting to evolving data landscapes.

Noisylabels addresses the challenge of training models on imperfect datasets, offering a significant advantage in scenarios where obtaining clean, error-free data is difficult or costly.