Propose a deep learning architecture that balances performance and computational efficiency for the pedestrian detection task. Explain how it addresses the limitations of previous speech recognition models in reducing error rates.

Use a state-of-the-art convolutional neural network (CNN) with transfer learning, fine-tuning pre-trained weights on our dataset to adapt the model to our specific task while minimizing the need for additional training data and computational resources. This approach addresses the limitations of previous speech recognition models by leveraging feature extraction capabilities that are less sensitive to noisy labels.