Which combination of steps will accomplish this goal with the LEAST amount of effort?

Explanations:

Reinitializing the weights of the entire CNN and retraining it from scratch would require significant effort and time. Instead, it is more efficient to retain the learned features from the previous model by only reinitializing the last fully connected layer.

Reinitializing the weights of the entire network and retraining it on the prediction task is not efficient. This would discard all previously learned representations in both the CNN and RNN, requiring extensive retraining with minimal benefit.

Reinitializing the weights of the entire RNN and retraining the entire model would similarly discard valuable learned representations from the CNN. This would lead to unnecessary computational costs and time without leveraging the previous model’s knowledge.

Reinitializing only the weights of the last fully connected layer of the CNN allows the model to retain previously learned features while adapting to the new set of objects. This method is efficient and requires less effort compared to retraining the entire CNN.

Reinitializing the weights of the last layer of the RNN and retraining the model on the prediction task is effective as it allows the RNN to adapt to the new sequence classification task while retaining the learned representations from the CNN. This approach minimizes the retraining effort while effectively adapting to the new use case.