Perturbation Artifact
Perturbation artifacts arise when manipulating input data or model parameters, unintentionally affecting model performance or interpretability beyond the intended effect. Current research focuses on understanding and mitigating these artifacts in various contexts, including adversarial attacks on deep learning models, reinforcement learning with noisy rewards, and the evaluation of feature importance methods. This work employs diverse approaches, such as developing robust model architectures (e.g., GAN extensions), designing loss functions to penalize harmful perturbations, and creating data augmentation techniques to improve model resilience. Addressing perturbation artifacts is crucial for building reliable and trustworthy AI systems across numerous applications.