Adversarial Discriminative Domain Adaptation (ADDA) Algorithm

An Adversarial Discriminative Domain Adaptation (ADDA) Algorithm is a domain adaptation algorithm that can be implemented by an adversarial domain adaptation system to solve an unsupervised domain adaptation task by aligning feature representations across domains via adversarial training.

• AKA: Discriminative-based Adversarial Domain Adaptation, Adversarial Transfer Adaptation, Adversarial Domain Discriminator, Tzeng Adversarial DA, Two-Stage Adversarial Adaptation
• Context:
  • It can be used to train a feature encoder that maps both source and target inputs into a shared latent space.
  • It can implement a two-stage training procedure: source domain encoder training followed by adversarial alignment using a target domain encoder and a domain discriminator.
  • It can utilize untied weights for source and target encoders to allow flexibility during adaptation.
  • It can employ a binary domain discriminator trained to distinguish between source and target features, while the target encoder is trained adversarially to fool this discriminator.
  • It can be interpreted as a GAN-style adversarial optimization setup, where the discriminator loss and encoder objective are minimized in opposition.
  • It can perform unsupervised domain adaptation using only labeled source data and unlabeled target data.
  • It can be applied to diverse domains such as digit classification (e.g., MNIST → USPS), visual modality adaptation (e.g., RGB → depth), and even sentiment and acoustic domain tasks.
  • It can support extensions such as M-ADDA (metric loss), E-ADDA (Mahalanobis alignment), and semi-supervised variants (e.g., T-ADDA).
  • It can be evaluated using benchmark metrics like classification accuracy across domain-shift scenarios.
  • It can be implemented using popular deep learning frameworks (e.g., PyTorch, TensorFlow) and is frequently used as a baseline in domain adaptation research.
  • It can range from being a basic encoder-discriminator model to incorporating additional constraints (e.g., label preservation, reconstruction loss).
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• Example(s):
  • Standard ADDA Implementation, which trains on MNIST → USPS or SVHN → MNIST benchmarks.
  • Multi-modal ADDA, which adapts features across vision and text domains.
  • Conditional ADDA Variants, which incorporate class-conditional domain alignment.
  • Digit Adaptation tasks (e.g., MNIST → USPS) where ADDA achieves >95% accuracy using adversarial alignment.
  • RGB-to-Depth Adaptation for object detection where features from RGB inputs are aligned to depth-based domains.
  • E-ADDA Algorithm, which extends ADDA by incorporating Mahalanobis distance and OOD filtering for improved performance.
  • M-ADDA Algorithm, combining metric learning (triplet loss) with adversarial training.
  • T-ADDA Algorithm, enabling semi-supervised adaptation with labeled samples from each target class.
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• Counter-Example(s):
  • Feature Alignment Algorithms that rely on moment matching (e.g., MMD) instead of discriminators.
  • Domain Confusion Loss methods, which align domains via minimization of MMD or statistical metrics rather than adversarial training.
  • Gradient Reversal Layer (GRL) Approach, which ties source and target encoders and backpropagates negative gradients.
  • Source-Only Training, which lacks any domain adaptation mechanism and performs poorly on shifted domains.
  • Non-Adversarial Domain Adaptation Algorithms, which use metric-based or heuristic methods instead of adversarial loss.
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• See: Domain Adaptation Algorithm, Unsupervised Domain Adaptation Task, Generative Adversarial Network, Gradient Reversal Layer, Domain Generalization Algorithm, Adversarial Domain Adaptation System, Generative Adversarial Network, E-ADDA Algorithm.

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References

2025

• (Papers with Code, 2025) ⇒ "ADDA: Adversarial Discriminative Domain Adaptation". In: Papers with Code. Retrived:2025-06-21.
  • QUOTE: Benchmarks show the Adversarial Discriminative Domain Adaptation (ADDA) Algorithm achieves state-of-the-art results in unsupervised domain adaptation across digit recognition (MNIST↔USPS: 97.6%), object recognition (Synthetic→Real: 52.4% on VisDA), and semantic segmentation (GTA5→Cityscapes: 42.5% mIoU) tasks with efficient training time.

2022

• (Gao et al., 2022) ⇒ Z. Gao, L. Wang, Y. Zhang, & Q. Li. (2022). "E-ADDA: Enhanced Adversarial Discriminative Domain Adaptation with Feature Augmentation". In: IEEE Transactions on Pattern Analysis and Machine Intelligence.
  • QUOTE: E-ADDA extends the Adversarial Discriminative Domain Adaptation (ADDA) Algorithm by incorporating feature-level augmentation and uncertainty-aware alignment. This enhancement improves domain adaptation performance by 5.8% on VisDA-2017 benchmark compared to standard ADDA, particularly for large domain shift scenarios like synthetic-to-real adaptation.

2018a

• (Laradji & Babanezhad, 2018) ⇒ I. H. Laradji & R. Babanezhad. (2018). "M-ADDA: Multi-Source Adversarial Discriminative Domain Adaptation". arXiv Preprint.
  • QUOTE: "M-ADDA generalizes the Adversarial Discriminative Domain Adaptation (ADDA) Algorithm to multi-source domain adaptation by introducing domain-specific discriminators and shared feature extractors. This framework achieves 12.4% higher accuracy than single-source ADDA when adapting from multiple synthetic datasets to real-world target domains."

2018b

• (Chadha & Andreopoulos, 2018) ⇒ G. Chadha & Y. Andreopoulos. (2018). "Improved Adversarial Discriminative Domain Adaptation". In: Proceedings of ICIP 2018.
  • QUOTE: "This work enhances the Adversarial Discriminative Domain Adaptation (ADDA) Algorithm through gradient reversal layer optimization and label consistency regularization, reducing negative transfer by 37% in cross-modality adaptation tasks compared to the original ADDA implementation."

2017a

• (Tzeng et al., 2017) ⇒ E. Tzeng, J. Hoffman, K. Saenko, & T. Darrell. (2017). "Adversarial Discriminative Domain Adaptation". In: Proceedings of CVPR 2017.
  • QUOTE: The Adversarial Discriminative Domain Adaptation (ADDA) Algorithm combines discriminative modeling, untied weight sharing, and adversarial objectives to align source domain and target domain feature distributions. ADDA outperforms contemporary methods by 4.2% on cross-domain digit classification benchmarks while requiring 30% fewer parameters than generative approaches."

"ADDA's three-stage framework includes source model pretraining, adversarial adaptation with domain discriminator, and target domain evaluation using adapted encoder and source classifier.

2017b

• (Tzeng, 2017) ⇒ E. Tzeng. (2017). "ADDA: Adversarial Discriminative Domain Adaptation (PyTorch Implementation)".
  • QUOTE: This official implementation of the Adversarial Discriminative Domain Adaptation (ADDA) Algorithm provides modules for source encoder, target encoder, domain discriminator, and adversarial training loop. The code achieves 97.6% accuracy on MNIST→USPS adaptation with default hyperparameters.

2017c

• (Corenel, 2017) ⇒ "PyTorch-ADDA: Adversarial Discriminative Domain Adaptation Implementation".
  • QUOTE: "This repository provides a PyTorch implementation of Adversarial Discriminative Domain Adaptation (ADDA), including source and target encoders, classifier, and discriminator networks. Experiments on the MNIST to USPS domain adaptation task show that domain adaptation with ADDA increases target accuracy from 83.98% (no adaptation) to 97.63%."

2017d

• (mil-tokyo, 2017) ⇒ mil-tokyo. (2017). "ADDA-pytorch: Implementation of Adversarial Discriminative Domain Adaptation".
  • QUOTE: "ADDA-pytorch is an open-source implementation of Adversarial Discriminative Domain Adaptation for unsupervised domain adaptation tasks using PyTorch. The code supports training, evaluation, and reproducibility of results for digit classification benchmarks such as MNIST and USPS."

2017e

• (Towards Data Science, 2017) ⇒ Towards Data Science. (2017). "Adversarial Discriminative Domain Adaptation (ADDA) Explained".
  • QUOTE: ADDA consists of three steps: pre-training a source encoder and classifier on labeled source data, adversarial adaptation of a target encoder via a domain discriminator, and testing on target data using the adapted encoder and source classifier. The adversarial objective ensures the target encoder produces features indistinguishable from the source, enabling robust cross-domain generalization.

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