Amazon Web Services AWS Certified Specialty MLS-C01 Question # 95 Topic 5 Discussion

To adapt a previously trained model to a new but related task efficiently, the best practice is to leverage transfer learning. This involves retaining the learned features from the earlier model and only retraining the final layers to accommodate the new classification categories.​

In the context of a hybrid architecture combining a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN):​

CNN Component: The CNN is responsible for extracting spatial features from video frames. Since the early layers of a CNN capture generic features like edges and textures, they are often transferable across tasks. Therefore, only the last fully connected layer, which maps these features to specific object classes, needs to be reinitialized and retrained for the new set of objects.​

RNN Component: The RNN handles the temporal dynamics of the sequence data. Similar to the CNN, the earlier layers of the RNN capture general sequence patterns. Thus, reinitializing and retraining only the last layer of the RNN allows the model to adapt to the new prediction task without the need to retrain the entire network.​

This approach minimizes training time and computational resources while effectively adapting the model to new tasks.