Feature-Critic Networks for Heterogeneous Domain Generalization

Abstract

The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training. Domain adaptation techniques alleviate this, but need some instances from the target domain to drive adaptation. Domain generalisation is the recently topical problem of learning a model that generalises to unseen domains out of the box, and various approaches aim to train a domain-invariant feature extractor, typically by adding some manually designed losses. In this work, we propose a learning to learn approach, where the auxiliary loss that helps generalisation is itself learned. Beyond conventional domain generalisation, we consider a more challenging setting of heterogeneous domain generalisation, where the unseen domains do not share label space with the seen ones, and the goal is to train a feature representation that is useful off-the-shelf for novel data and novel categories. Experimental evaluation demonstrates that our method outperforms state-of-the-art solutions in both settings.

Presented at ICML 2019

References in corpus (6)

• How transferable are features in deep neural networks?
• On the Convergence of Adam and Beyond
• Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
• Domain Separation Networks
• Domain Generalization via Invariant Feature Representation
• Learning to Learn: Meta-Critic Networks for Sample Efficient Learning

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• FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space
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• Learning to Learn with Variational Information Bottleneck for Domain Generalization
• A Fourier-based Framework for Domain Generalization
• Open Domain Generalization with Domain-Augmented Meta-Learning
• Learning Invariant Representations across Domains and Tasks
• An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset
• Provable Adaptation across Multiway Domains via Representation Learning
• Towards Recognizing New Semantic Concepts in New Visual Domains
• Explainability-aided Domain Generalization for Image Classification
• Domain Agnostic Learning for Unbiased Authentication