Enhanced Robustness

Enhanced robustness in machine learning focuses on developing models resilient to various forms of noise, adversarial attacks, and distributional shifts, aiming for reliable performance in real-world scenarios. Current research explores diverse techniques, including multi-norm training, variational sparsification, parameter-efficient fine-tuning (like LoRA), randomized smoothing, and adversarial training methods (e.g., phase-shifted adversarial training), often applied to various architectures such as convolutional neural networks and vision transformers. These advancements are crucial for deploying machine learning models in safety-critical applications and improving their generalizability across diverse datasets and environments. The ultimate goal is to create models that are not only accurate but also consistently reliable and trustworthy.