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Related Questions
- What are some techniques for optimizing model architecture to reduce computational costs while maintaining realistic synthetic examples?
- How can data augmentation and oversampling be used to increase the diversity of synthetic examples without significantly increasing computational costs?
- What are some strategies for using transfer learning to leverage pre-trained models and reduce the computational costs of generating realistic synthetic examples?
- Can you explain the concept of 'synthetic to real' ratio and how it can be used to balance the trade-off between realism and computational costs?
- How can we use techniques like Monte Carlo dropout and ensemble methods to generate more realistic synthetic examples while reducing the computational costs?
- What are some methods for pruning or compressing large models to reduce computational costs without sacrificing realism?
- Can you discuss the use of approximation techniques like variational inference or stochastic gradient descent to balance the trade-off between realism and computational costs?
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