Adversarial Training and Robustness
Adversarial training is a method of training deep learning models to be robust to changes in the input data. It is a key component of natural language processing (NLP) models, which often need to be able to handle noisy and uncertain data. In this guide, we will explain what adversarial training is, how it works, and provide three examples of its use in NLP. We will also include some tips for getting the most out of adversarial training.
What is Adversarial Training?
Adversarial training is a technique for training models to be resistant to changes in the input data. It works by introducing small changes to the input data, referred to as “adversarial examples”, during training. The model is then trained to recognize and respond appropriately to these perturbed inputs. This allows the model to become more robust to changes in the input data, such as noise, typos, or other variations.
How Does Adversarial Training Work?
Adversarial training is based on the idea that deep learning models are vulnerable to small changes in the input data. By introducing these small changes, referred to as “adversarial examples”, during training, the model is forced to learn to recognize and respond appropriately to these perturbed inputs.
The adversarial examples are generated by applying a small perturbation to the input data. This perturbation can be any small change that would be imperceptible to humans, such as adding a small amount of noise or randomly flipping a few pixels. The model is then trained to recognize and respond appropriately to these perturbed inputs.
Examples of Adversarial Training in NLP
1. One example of adversarial training in NLP is the use of data augmentation. Data augmentation is a technique for generating new training data by introducing small changes to existing training data. This can include adding noises or typos to text, or randomly changing the order of words in a sentence.
2. Another example of adversarial training in NLP is the use of adversarial attack and defense. This is a technique for generating adversarial examples by deliberately introducing small changes to the input data. This can include adding typos or synonyms, or using different word ordering in a sentence. The model is then trained to recognize and respond appropriately to these perturbed inputs.
3. A third example of adversarial training in NLP is the use of domain adaptation. Domain adaptation is a technique for training models to be robust to changes in the data distribution. In NLP, this can include training a model to be robust to changes in the language or dialect of the input data.
Tips for Getting the Most Out of Adversarial Training
1. Make sure to use a variety of different perturbations when generating adversarial examples. This will help the model to become more robust to changes in the input data.
2. Be sure to include a large number of adversarial examples in your training data. This will help the model to better recognize and respond appropriately to perturbed inputs.
3. Monitor the performance of your model on adversarial examples during training. This will help you to identify any areas where your model is not performing well and adjust the training accordingly.
Conclusion
Adversarial training is an important technique for training deep learning models to be robust to changes in the input data. It works by introducing small changes to the input data, referred to as “adversarial examples”, during training. We have provided three examples of how adversarial training can be used in NLP, as well as some tips for getting the most out of adversarial training. By using adversarial training, you can ensure that your NLP models are robust to changes in the input data.