Comparative analysis – Transformer versus RNN models
When comparing Transformer models to RNN models, we’re contrasting two fundamentally different approaches to processing sequence data, each with its unique strengths and challenges. This section will provide a comparative analysis of these two types of models:
- Performance on long sequences: Transformers generally outperform RNNs on tasks involving long sequences because of their ability to attend to all parts of the sequence simultaneously
- Training speed and efficiency: Transformers can be trained more efficiently on hardware accelerators such as GPUs and TPUs due to their parallelizable architecture
- Flexibility and adaptability: Transformers have shown greater flexibility and have been successfully applied to a wider range of tasks beyond sequence processing, including image recognition and playing games
- Data requirements: RNNs can sometimes be more data-efficient, requiring less data to reach good...
