Unsupervised and Self-Supervised Pretraining
Explore the significance of unsupervised and self-supervised pretraining for transformers and its pivotal role.
Next, let's focus on a crucial aspect of transformers—unsupervised and self-supervised pretraining. This aspect is especially significant as we navigate the complexities of training a massive model.
Scalability to learn from a large dataset
A key advantage of transformers is their scalability when learning from a large dataset. Unlike convolutional or recurrent models, transformers operate without making strong assumptions about the problem's structure, allowing them to handle diverse datasets effectively.
Their capacity to accommodate more weights without specific model assumptions makes transformers well-suited for pretraining on massive datasets with fewer requirements. This pretraining phase can take place in an unsupervised or self-supervised manner, enabling the model to learn and capture meaningful patterns from the vast amount of data available.
Why pretraining is essential
With minimal model assumptions, we minimize any inductive biases. We don't incorporate prior knowledge regarding the model graph's connectivity or the problem's structure, in contrast to convolutional and ...