Generalizing Across Domains via Cross-Gradient Training (1804.10745v2)

Abstract: 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 parallelly 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 that (2) data augmentation is a more stable and accurate method than domain adversarial training.

• The paper introduces CrossGrad, a technique that leverages domain-guided perturbations for effective generalization without requiring domain-specific adaptation.
• It employs a Bayesian framework with simultaneous training of label and domain classifiers to robustly augment inputs.
• Empirical evaluations reveal superior performance over traditional methods in tasks like handwriting and spoken word recognition.

Cross-Gradient Training for Domain Generalization

The paper presents CrossGrad, a novel technique for developing classifiers that generalize effectively across domains. Unlike existing methods that necessitate domain adaptation phases involving labeled or unlabeled data from new domains, CrossGrad is designed to bypass this requirement. This makes it applicable to a wide range of applications like handwriting recognition and sentiment analysis, where domain characteristics such as fonts or speakers may vary.

Methodology

The CrossGrad technique is built upon a Bayesian model, utilizing domain-guided perturbations for data augmentation. It involves simultaneously training a label classifier and a domain classifier, each influencing the other through perturbations derived from loss gradients. This approach allows for the direct perturbation of inputs, enabling CrossGrad to exploit domain-specific signals for better label predictions while avoiding overfitting on training domains.

Key Insights

1. Data Augmentation: CrossGrad performs data augmentation by inducing input variations aligned with domain characteristics. This is achieved by perturbing the input along the gradient of the domain loss, which allows the model to better navigate unseen domains.
2. Bayesian Framework: The problem is formulated within a Bayesian framework, where domain perturbations are conceptualized as sampling steps. This facilitates a more systematic approach to handling domain variations and label predictions.
3. Empirical Evaluation: CrossGrad's efficacy is validated across three applications: character recognition, handwriting recognition, and spoken word recognition. In each case, the proposed method demonstrates superior generalization to new domains compared to traditional approaches, confirming the value of domain-guided perturbations over generic ones.

Experimental Results

The paper provides robust empirical evidence supporting the proposed technique. Specific numerical results show that CrossGrad consistently outperforms domain adversarial networks (DANs) and label adversarial perturbations across all tasks. For instance, it achieves higher accuracy on new domains without requiring any domain-specific adaptation phase—a significant advantage over existing methods.

Implications and Future Directions

The research suggests several practical and theoretical implications:

• Practical Applications: CrossGrad could significantly benefit real-world applications that encounter diverse domain shifts in their data, enhancing the robustness and reliability of deployed models.
• Theoretical Contributions: The method challenges the prevailing notion in domain adaptation literature that domain signals should be entirely erased. Instead, it proposes a nuanced approach, effectively finding a balance between exploiting and mitigating domain influences.
• Future Research: Potential areas for further exploration include investigating integration with unlabeled data from target domains and extending the CrossGrad approach to more complex models or settings. Moreover, bridging the technique with other adversarial training frameworks could yield additional improvements in domain generalization.

Conclusion

CrossGrad introduces a compelling strategy for domain generalization by employing a cross-gradient training approach. Its ability to generalize to unseen domains without requiring additional adaptation phases marks a significant step forward in the development of more robust classifiers across various domain-centric applications. The paper opens avenues for future research that could further refine these methodologies, contributing valuable insights to the field of machine learning and AI.