Vision transformer-based meta loss landscape exploration with actor-critic method

Detecting and mitigating overfitting in deep neural networks remains a critical challenge in modern machine learning. This paper investigates innovative approaches to address these challenges, particularly focusing on vision transformer-based models. By leveraging meta-learning techniques and reinforcement learning frameworks, we introduce transformer-based loss landscape exploration (TLLE), which utilizes the validation loss landscape to guide gradient descent optimization. Unlike conventional methods, TLLE employs the actor-critic algorithm to learn the mapping from model weights to future values, facilitating efficient sample collection and precise value predictions. Experimental results demonstrate the superior performance of TLLE-enhanced transformer models in image classification and segmentation tasks, showcasing the efficacy of our approach in optimizing deep learning models for image analysis.