Enforced Adversarial Discriminative Domain Adaptation (E-ADDA) Algorithm

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A Enforced Adversarial Discriminative Domain Adaptation (E-ADDA) Algorithm is an adversarial domain adaptation algorithm that can be implemented by an adversarial domain adaptation system to solve an unsupervised domain adaptation task by enhancing feature alignment via Mahalanobis distance enforcement and out-of-distribution filtering.

  • AKA: Enforced ADDA Algorithm, Enhanced ADDA, Mahalanobis-Enforced ADDA.
  • Context:
    • It can extend the ADDA Algorithm by incorporating an additional Mahalanobis distance loss to enforce tighter class-level feature alignment across domains.
    • It can employ an out-of-distribution (OOD) filtering step to reduce the influence of low-confidence or irrelevant target samples during adversarial training.
    • It can be trained in two stages: first on labeled source data to learn a source encoder and classifier, and then with adversarial training on the target encoder using both a discriminator and the Mahalanobis alignment objective.
    • It can improve domain generalization in both visual and acoustic domains by enforcing discriminative alignment beyond adversarial loss.
    • It can outperform baseline ADDA and other domain adaptation algorithms on standard tasks such as digit classification, cross-domain object recognition, and audio-based classification.
    • It can be implemented using deep learning frameworks and adapted for tasks with large domain gaps or noisy unlabeled target data.
    • It can support further extensions such as multi-class Mahalanobis alignment, conditional discriminators, or metric-guided alignment for partial domain adaptation.
    • It can leverage benchmark datasets like MNIST, USPS, Office-31, Office-Home, STL-10, CIFAR-10, and acoustic corpora for training and evaluation.
    • It can be used as a drop-in replacement for ADDA in tasks requiring enhanced domain discrimination and class-conditional feature consistency.
    • ...
  • Example(s):
  • Counter-Example(s):
  • See: ADDA Algorithm, Unsupervised Domain Adaptation Task, Mahalanobis Distance, Out-of-Distribution Detection, Domain Adaptation Benchmark, Adversarial Domain Adaptation System.


References

2023a

2022a

2022b

2022c

2018