Domain-Invariant Feature Representation

A Domain-Invariant Feature Representation is a feature representation that captures task-relevant information while minimizing sensitivity to domain-specific variations, enabling models to generalize across different data distributions.

• AKA: Domain-Independent Feature Representation, Transferable Feature Representation, Cross-Domain Feature Representation.
• Context:
  • It can be systematically extracted through:
    • Adversarial training, to align feature distributions across domains by minimizing domain classification accuracy.
    • Maximum Mean Discrepancy (MMD), to reduce distributional divergence between source and target domains.
    • Domain-Invariant Component Analysis (DICA), to learn transformations that minimize domain variance while preserving task-related information.
    • Domain-Invariant Feature Exploration (DIFEX), to identify features that are both internally and mutually invariant across domains.
  • It can be utilized in unsupervised domain adaptation to adapt models trained on labeled source data to unlabeled target domains.
  • It can enhance domain generalization by enabling models to perform well on unseen domains without additional training.
  • It can be applied in various fields such as computer vision, natural language processing, and speech recognition where domain shifts are common.
  • It can be integrated into deep neural networks as intermediate representations that facilitate cross-domain learning.
  • It can be evaluated using metrics like domain classification accuracy and task performance on target domains.
  • It can be visualized using techniques like t-SNE to assess the alignment of feature distributions across domains.
  • ...
• Example(s):
  • Utilizing adversarial training to learn domain-invariant features for sentiment analysis across different languages.
  • Applying MMD to align features in image classification tasks between synthetic and real-world datasets.
  • Implementing DICA to extract invariant components for object recognition across varying lighting conditions.
  • ...
• Counter-Example(s):
  • Domain-Specific Feature, which captures information unique to a particular domain and may not generalize well.
  • Raw Input Feature, which may contain domain-dependent noise and biases.
  • Overfitted Feature Representation, which performs well on the training domain but poorly on unseen domains.
  • ...
• See: Domain Adaptation, Transfer Learning, Domain Generalization, Adversarial Training, Maximum Mean Discrepancy, Domain-Invariant Component Analysis, Domain-Specific Text Understanding Task, Automated Domain-Specific Writing Task.

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References

2024

• (Jiang et al., 2024) ⇒ L. Jiang, J. Wu, S. Zhao, Y. Liu, & J. Wang. (2024). "Domain-invariant feature learning with label information integration for cross-domain classification". In: Neural Computing and Applications.
  • QUOTE: Domain-invariant feature learning with label information integration (DILI) integrates metric learning and label information extraction to learn a cross-domain discriminant subspace. DILI reduces distances between source domain and target domain samples to mitigate marginal distribution discrepancy, and further reduces distances between cross-domain samples from the same class to address conditional distribution discrepancy. Dual terms balance label information of both domains, and a discriminant subspace is learned for cross-domain tasks. Experimental results on eight cross-domain datasets show that DILI outperforms state-of-the-art methods.

2022

• (Wang et al., 2022) ⇒ Jindong Wang, Yue Zhang, Yiqiang Chen, Wen Li, Mingsheng Long, Qiang Yang, & Philip S. Yu. (2022). "Domain-invariant Feature Exploration for Domain Generalization". arXiv Preprint.
  • QUOTE: We argue that domain-invariant features should originate from both internal invariance (features learned within a single domain) and mutual invariance (features learned across multiple domains). We propose DIFEX for Domain-Invariant Feature EXploration, employing a knowledge distillation framework to capture high-level Fourier phase as internally-invariant features and learn cross-domain correlation alignment as mutually-invariant features. An exploration loss increases feature diversity for better generalization. Extensive experiments on time-series and visual benchmarks demonstrate that DIFEX achieves state-of-the-art performance.
• (V7 Labs, 2022) ⇒ V7 Labs. (2022). "Domain Adaptation in Computer Vision: Everything You Need to Know".
  • QUOTE: Domain adaptation is a technique to improve model performance on a target domain with insufficient annotated data by using knowledge learned from a related source domain with adequate labeled data. Domain adaptation is a special case of transfer learning. The mechanism involves uncovering common latent factors across source and target domains and adapting them to reduce both marginal and conditional mismatch in the feature space. Techniques include feature alignment, classifier adaptation, and approaches for homogeneous and heterogeneous domain adaptation.

2013

• (Muandet et al., 2013) ⇒ Krikamol Muandet, David Balduzzi, & Bernhard Schölkopf. (2013). "Domain Generalization via Invariant Feature Representation". In: Proceedings of ICML 2013.
  • QUOTE: We introduce a domain generalization framework that learns invariant feature representations from multiple source domains to improve generalization to unseen target domains. Our method minimizes the distributional variance of feature representations across domains, leading to improved robustness in domain shift scenarios.

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