Learn the most efficient steps to adapt a previously trained CNN-RNN model for classifying and predicting new objects in video sequences. Save time by reusing and fine-tuning the existing model.
Table of Contents
Question
A machine learning (ML) specialist is developing a model for a company. The model will classify and predict sequences of objects that are displayed in a video. The ML specialist decides to use a hybrid architecture that consists of a convolutional neural network (CNN) followed by a classifier three-layer recurrent neural network (RNN).
The company developed a similar model previously but trained the model to classify a different set of objects. The ML specialist wants to save time by using the previously trained model and adapting the model for the current use case and set of objects.
Which combination of steps will accomplish this goal with the LEAST amount of effort? (Choose two.)
A. Reinitialize the weights of the entire CNN. Retrain the CNN on the classification task by using the new set of objects.
B. Reinitialize the weights of the entire network. Retrain the entire network on the prediction task by using the new set of objects.
C. Reinitialize the weights of the entire RNN. Retrain the entire model on the prediction task by using the new set of objects.
D. Reinitialize the weights of the last fully connected layer of the CNN. Retrain the CNN on the classification task by using the new set of objects.
E. Reinitialize the weights of the last layer of the RNN. Retrain the entire model on the prediction task by using the new set of objects.
Answer
The most efficient combination of steps to adapt the previously trained CNN-RNN model for the new set of objects with the least effort is:
D. Reinitialize the weights of the last fully connected layer of the CNN. Retrain the CNN on the classification task by using the new set of objects.
E. Reinitialize the weights of the last layer of the RNN. Retrain the entire model on the prediction task by using the new set of objects.
Explanation
When adapting a previously trained model for a new but similar task, the most efficient approach is to leverage transfer learning. This involves reusing the learned features and weights from the earlier layers of the model, which often capture general and transferable patterns, while fine-tuning the later layers to adapt to the specific new task.
For the CNN part, reinitializing and retraining only the last fully connected layer (choice D) is sufficient. The convolutional layers have already learned to extract relevant visual features, which can be reused. By retraining the last layer on the new set of objects, the CNN will learn to map those features to the correct object classes.
For the RNN part, reinitializing the weights of just the last layer (choice E) allows it to adapt to predicting sequences of the new objects. The RNN layers have captured temporal patterns that can still be leveraged. Retraining the entire CNN-RNN model on the prediction task with the new data will fine-tune both components together.
Choices A, B and C are less efficient as they involve reinitializing and retraining larger portions of the model from scratch, discarding more of the previously learned knowledge.
In summary, the combination of fine-tuning the last CNN layer for classification and last RNN layer for prediction, while retraining the full model, will efficiently adapt it for the new objects with minimal effort by maximizing the reuse of pre-learned features. This transfer learning approach saves significant time and computational resources compared to training a new model from the ground up.
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