Injection Based Training

Injection-based training encompasses various techniques that introduce noise or perturbations during model training to enhance robustness and efficiency. Current research focuses on improving model accuracy and resilience against adversarial attacks (including universal perturbations), optimizing training for low-resource scenarios (like accented speech recognition), and developing energy-efficient training methods through network pruning. These advancements are significant for improving the reliability and practicality of machine learning models across diverse applications, ranging from cybersecurity to resource-constrained environments.