How would you optimize the performance of a deep learning model in TensorFlow or PyTorch during the inference stage?
- A. Quantization
- B. Data Augmentation
- C. Gradient Clipping
- D. Model Initialization
Option A, Quantization, is a common optimization technique during the inference stage. It involves reducing the precision of model weights and activations, leading to smaller memory usage and faster inference. Option B, Data Augmentation, is typically used during training, not inference. Option C, Gradient Clipping, is a training technique to prevent exploding gradients. Option D, Model Initialization, is essential for training but less relevant during inference.
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