Accuracy Efficiency Trade

The accuracy-efficiency trade-off in deep learning focuses on optimizing model performance while minimizing computational resources. Current research emphasizes techniques like model compression (e.g., pruning, quantization), efficient architectures (e.g., Mixture of Experts, single-gated MoEs), and dynamic resource allocation (e.g., prioritizing high-quality predictions), applied across various model types including large language models and video frame interpolators. This research is crucial for deploying advanced models on resource-constrained devices and promoting sustainable and equitable AI development by reducing energy consumption and computational costs. Ultimately, this work aims to improve the accessibility and practicality of powerful deep learning models.