Given a scenario where computational resources are limited, but there's a need to process high-resolution images for feature detection, what approach might be taken in the design of the neural network to balance performance and computational efficiency?
- Use Transfer Learning
- Increase Network Depth
- Add More Neurons
- Use Recurrent Connections
Transfer Learning can balance performance and computational efficiency by leveraging pre-trained models on high-resolution images, reducing the need for extensive training.
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