Which of the following is a CORRECT description of how transfer learning is applied in image processing neural networks? A. A pre-trained base model is used for the convolutional part of the network,...

I need the answer as soon as possibleWhich of the following is a CORRECT<br>description of how transfer learning is applied in<br>image processing neural networks?<br>A. A pre-trained base model is used for the<br>convolutional part of the network, with a<br>custom trained top/flat layer section to map the<br>convolutional output to final predictions<br>B. A pre-trained set of weights is used for the<br>top/flat layer section of the network, but custom<br>trained filters are used for the convolutional part<br>of the model<br>C. A pre-trained base model is used to generate<br>predictions for the new image dataset, and<br>those predictions are manually mapped to the<br>new dataset's target labels<br>D. All model weights are trained from scratch,<br>but these parameters are constrained to be<br>close to the pre-trained weights<br>

Extracted text: Which of the following is a CORRECT description of how transfer learning is applied in image processing neural networks? A. A pre-trained base model is used for the convolutional part of the network, with a custom trained top/flat layer section to map the convolutional output to final predictions B. A pre-trained set of weights is used for the top/flat layer section of the network, but custom trained filters are used for the convolutional part of the model C. A pre-trained base model is used to generate predictions for the new image dataset, and those predictions are manually mapped to the new dataset's target labels D. All model weights are trained from scratch, but these parameters are constrained to be close to the pre-trained weights

Jun 06, 2022
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