|
| | | | |
| | | |
| Stat |
Members: 3657
Articles: 2'599'751
Articles rated: 2609
06 October 2024 |
|
| | | | |
|
Article overview
| |
|
|
IDANI: Inference-time Domain Adaptation via Neuron-level Interventions | | Omer Antverg
; Eyal Ben-David
; Yonatan Belinkov
; | | Date: |
1 Jun 2022 | | Abstract: | Large pre-trained models are usually fine-tuned on downstream task data, and
tested on unseen data. When the train and test data come from different
domains, the model is likely to struggle, as it is not adapted to the test
domain. We propose a new approach for domain adaptation (DA), using
neuron-level interventions: We modify the representation of each test example
in specific neurons, resulting in a counterfactual example from the source
domain, which the model is more familiar with. The modified example is then fed
back into the model. While most other DA methods are applied during training
time, ours is applied during inference only, making it more efficient and
applicable. Our experiments show that our method improves performance on unseen
domains. | | Source: | arXiv, 2206.00259 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
|
| |
|
|
|
REGISTER