What is model ensembling in Computer Vision and why does it improve performance?

Ensembling combines multiple models to reduce variance and improve generalization.

Different models capture different aspects of data distribution. Ensembling aggregates predictions via averaging, voting, or weighted fusion. This reduces overfitting and improves robustness. In vision tasks, ensembles often combine different architectures like ResNet, EfficientNet, and ViT.