Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

We present CROSSGRAD , a method to use multi-domain training data to learn a classifier that generalizes to new domains.
CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain.
Most existing domain adaptation methods attempt to erase domain signals using techniques like domain adversarial training.
In contrast, CROSSGRAD is free to use domain signals for predicting labels, if it can prevent overfitting on training domains.
We conceptualize the task in a Bayesian setting, in which a sampling step is implemented as data augmentation, based on domain-guided perturbations of input instances.
CROSSGRAD jointly trains a label and a domain classifier on examples perturbed by loss gradients of each other’s objectives.
This enables us to directly perturb inputs, without separating and re-mixing domain signals while making various distributional assumptions.
Empirical evaluation on three different applications where this setting is natural establishes that

 (1) domain-guided perturbation provides consistently better generalization to unseen domains, compared to generic instance perturbation methods, and 

(2) data augmentation is a more stable and accurate method than domain adversarial training.
We investigate how to train a classification model using multi-domain training data, so as to generalize to labeling instances from unseen domains.
This problem arises in many applications, viz., handwriting recognition, speech recognition, sentiment analysis, and sensor data interpretation.
In these applications, domains may be defined by fonts, speakers, writers, etc.
Most existing work on handling a target domain not seen at training time requires either labeled or unlabeled data from the target domain at test time.
Often, a separate "adaptation" step is then run over the source and target domain instances, only after which target domain instances are labeled.
In contrast, we consider the situation where, during training, we have labeled instances from several domains which we can collectively exploit so that the trained system can handle new domains without the adaptation step.
Domain d and label y interact in complicated ways to influence the observable input x.
Most domain adaption strategies implicitly consider the domain signal to be extraneous and seek to remove its effect to train more robust label predictors.
We presented CROSSGRAD, which considers them in a more symmetric manner.
CROSSGRAD provides a new data augmentation scheme based on the y (respectively, d) predictor using the gradient of the d (respectively, y) predictor over the input space, to generate perturbations.
Experiments comparing CROSSGRAD with various recent adversarial paradigms show that CROSSGRAD can make better use of partially correlated y and d, without requiring explicit distributional assumptions about how they affect x.
CROSSGRAD is at its best when training domains are scarce and do not directly cover test domains well.
Future work includes extending CROSSGRAD to exploit labeled or unlabeled data in the test domain, and integrating the best of LABELGRAD and CROSSGRAD into a single algorithm.Hence, the initial gradient descent step to affect a change of ∆ĝ in the domain features would increment x by J ∆ĝ.
The Jacobian, which is a matrix of first partial derivatives, can be computed by back-propagation.
Thus we get DISPLAYFORM0 which, by the chain rule, gives DISPLAYFORM1
Domain guided augmentation of data provides a robust and stable method of domain generalization