Hidden Representation Perturbation

Hidden representation perturbation involves subtly altering the internal representations of machine learning models to analyze their robustness, improve performance, or mitigate biases. Current research focuses on applying this technique to various model architectures, including transformers, generative adversarial networks (GANs), and graph neural networks (GNNs), using methods like adversarial attacks, generative perturbations, and channel pruning to achieve specific goals such as fairness enhancement, improved generalization, and efficient compression. This research is significant because it offers valuable insights into model vulnerabilities, facilitates the development of more robust and equitable AI systems, and enables optimization strategies for computationally expensive models.