Deep Companion

"Deep Companion" research explores methods for improving the performance and efficiency of deep learning models, primarily focusing on enhancing generalization and reducing computational costs. Current efforts involve developing companion models that provide targeted feedback during training (e.g., by identifying challenging scenarios) or act as lightweight pre-filters to reduce the workload on larger models, employing architectures like EfficientViT and EfficientNet. This work has significant implications for advancing the capabilities of AI systems while mitigating the resource demands associated with increasingly complex models, impacting fields ranging from image classification to federated learning.