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What is Adapter

Houlsby et al. (2019) introduced adapters as an alternative approach for adaptation in transfer learning in NLP within deep transformer-based architectures. Adapters are task-specific neural modules that are added between layers of a pre-trained network. After coping weights from a pre-trained network, pre-trained weights will be frozen, and only Adapters will be trained.

Why Adapter?

Adapters provide numerous benefits over plain fully fine-tuning or other approaches that result in compact models such as multi-task learning:

• It is a lightweight alternative to fully fine-tuning that trains only a few trainable parameters per task without sacrificing performance.
• Yielding a high degree of parameter sharing between down-stream tasks due to being frozen of original network parameters.
• Unlike multi-task learning that requires simultaneous access to all tasks, it allows training on down-stream tasks sequentially. Thus, adding new tasks do not require complete joint retraining. Further, eliminates the hassle of weighing losses or balancing training set sizes.
• Training adapters for each task separately, leading to that the model not forgetting how to perform previous tasks (the problem of catastrophic forgetting).

Learn more in the paper "Parameter-Efficient Transfer Learning for NLP".