Enforced Adversarial Discriminative Domain Adaptation (E-ADDA) Task

A Enforced Adversarial Discriminative Domain Adaptation (E-ADDA) Task is an unsupervised domain adaptation task that refers to evaluating the performance of an E-ADDA System or Enforced ADDA Algorithm in aligning source and target domains using adversarial and Mahalanobis-guided techniques.

• AKA: E-ADDA Task, Enforced ADDA Evaluation Task, Mahalanobis-Based Domain Adaptation Task.
• Context:
  • Task Input: Labeled source domain data and unlabeled target domain data.
    • Optional Input: Class structure priors, OOD thresholds.
  • Task Output: Target-domain predictions (class labels or scores).
  • Task Performance Measures: Accuracy, F1-score, precision-recall, target alignment gap.
  • Task Objective: Evaluate the ability of a system to align feature distributions and improve performance on target domain examples.
  • It can be systematically solved and automated by an E-ADDA System.
  • It can be instantiated across visual (image classification), acoustic (emotion → conflict), and text (sentiment → polarity) domains.
  • It can compare performance against baselines such as ADDA, MMD-based methods, and GRL-style systems.
  • It can involve robustness metrics to test for OOD filtering efficiency and domain gap reduction.
  • It can measure the impact of Mahalanobis loss on within-class variance reduction and inter-class separability across domains.
  • ...
• Example(s):
  • MNIST → USPS E-ADDA Task, measuring post-adaptation classification accuracy.
  • STL-10 → CIFAR-10 Transfer Task, using E-ADDA to improve class alignment.
  • Office-Home E-ADDA Task, where Mahalanobis loss enhances target performance.
  • Acoustic Emotion Adaptation Task, evaluating E-ADDA in conflict detection.
  • ...
• Counter-Example(s):
  • ADDA Evaluation Task, which excludes Mahalanobis enforcement.
  • In-Domain Classification Task, which doesn’t involve domain shift or adaptation.
  • Domain Generalization Task that lacks any labeled source domain.
  • ...
• See: Enforced ADDA Algorithm, Unsupervised Domain Adaptation Task, E-ADDA System, Domain Adaptation Benchmark, Mahalanobis Distance.

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References

2023a

• (Papers with Code, 2023) ⇒ Papers with Code. (2023). "E-ADDA: Unsupervised Adversarial Domain Adaptation Enhanced by Mahalanobis Distance Loss".
  • QUOTE: "The Enforced Adversarial Discriminative Domain Adaptation Algorithm (E-ADDA) achieves state-of-the-art performance on Office-31 (91.2% accuracy) and Office-Home (72.3% accuracy) benchmarks, outperforming prior methods by up to 17.9%. Key innovations include a Mahalanobis distance loss that minimizes distributional divergence between source domain and target domain embeddings, and an out-of-distribution detection subroutine that filters samples resistant to domain alignment."

2022a

• (Gao et al., 2022a) ⇒ Ye Gao, Brian Baucom, Karen Rose, Kristina Gordon, Hongning Wang, & John A. Stankovic. (2022). "E-ADDA: Unsupervised Adversarial Domain Adaptation Enhanced by a New Mahalanobis Distance Loss for Smart Computing". arXiv Preprint.
  • QUOTE: "The Enforced Adversarial Discriminative Domain Adaptation Algorithm (E-ADDA) enhances ADDA by introducing a Mahalanobis distance loss that explicitly minimizes the distribution-wise distance between encoded target samples and the source domain distribution. This loss, defined as \( L_{M} = \mathbb{E}_{x_t \sim \mathcal{T}} \left[ (f_t(x_t) - \mu_s)^T \Sigma_s^{-1} (f_t(x_t) - \mu_s) \right] \), enforces additional domain confusion beyond standard adversarial training. Combined with out-of-distribution detection, E-ADDA improves acoustic modality adaptation by 29.8% F1 over baseline methods."

2022b

• (Gao et al., 2022b) ⇒ Ye Gao, Brian Baucom, Karen Rose, Kristina Gordon, Hongning Wang, & John A. Stankovic. (2022). "E-ADDA for Acoustic Domain Adaptation in Conflict Speech Detection". In: Proceedings of LREC 2022.
  • QUOTE: "When applied to acoustic domain adaptation from EMOTION dataset to CONFLICT dataset, the Enforced Adversarial Discriminative Domain Adaptation Algorithm achieved 93.1% F1 score under environmental distortions—surpassing ADDA (38.3%) and ADDA+CORAL (63.3%). The Mahalanobis distance loss effectively reduced domain shift in overlapped speech scenarios while the OOD detection module filtered 22% of misaligned target samples."

2022c

• (W. Gao, 2022) ⇒ Wenjing Gao. (2022). "Unofficial PyTorch Implementation of E-ADDA".
  • QUOTE: This implementation of the Enforced Adversarial Discriminative Domain Adaptation Algorithm includes modular source encoder, target encoder, domain discriminator, and Mahalanobis distance loss components. The code reproduces 91.8% accuracy on Office-31's **A→W** task using ResNet-50 backbones, validating the original paper's results.

2018

• (Laradji & Babanezhad, 2018) ⇒ I. H. Laradji & R. Babanezhad. (2018). "M-ADDA: Multi-Source Adversarial Discriminative Domain Adaptation". arXiv Preprint.
  • QUOTE: Unlike the single-source focus of E-ADDA, M-ADDA extends ADDA to multi-source domain adaptation using domain-specific discriminators and shared feature extractors. This contrast highlights E-ADDA's innovation in enforcing intra-domain distribution alignment via Mahalanobis metrics rather than multi-source aggregation.

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