# Coupling Adversarial Learning with Selective Voting Strategy for Distribution Alignment in Partial Domain Adaptation

arXiv: https://arxiv.org/abs/2207.08145 · HTML: https://mlsanity.com/paper/2207.08145/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 20 references that are in the map.
- First posted on arXiv 17 Jul 2022.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017)
- [Unsupervised Domain Adaptation with Residual Transfer Networks](https://mlsanity.com/paper/1602.04433/) (2016)
- [Partial Adversarial Domain Adaptation](https://mlsanity.com/paper/1808.04205/) (2018)
- [Importance Weighted Adversarial Nets for Partial Domain Adaptation](https://mlsanity.com/paper/1803.09210/) (2018)
- [Learning to Transfer Examples for Partial Domain Adaptation](https://mlsanity.com/paper/1903.12230/) (2019)
- [Partial Transfer Learning with Selective Adversarial Networks](https://mlsanity.com/paper/1707.07901/) (2017)
- [LSDA: Large Scale Detection Through Adaptation](https://mlsanity.com/paper/1407.5035/) (2014)
- [Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation](https://mlsanity.com/paper/2008.11873/) (2020)
- [Partial Domain Adaptation Using Selective Representation Learning For Class-Weight Computation](https://mlsanity.com/paper/2101.02275/) (2021)

## Built on by (most cited first, 3 of 3)

- [Domain-Invariant Feature Alignment Using Variational Inference For Partial Domain Adaptation](https://mlsanity.com/paper/2212.01590/) (2022)
- [A Robust Negative Learning Approach to Partial Domain Adaptation Using Source Prototypes](https://mlsanity.com/paper/2309.03531/) (2023)
- [Robust Class-Conditional Distribution Alignment for Partial Domain Adaptation](https://mlsanity.com/paper/2310.12060/) (2023)

## Abstract (arXiv)

In contrast to a standard closed-set domain adaptation task, partial domain adaptation setup caters to a realistic scenario by relaxing the identical label set assumption. The fact of source label set subsuming the target label set, however, introduces few additional obstacles as training on private source category samples thwart relevant knowledge transfer and mislead the classification process. To mitigate these issues, we devise a mechanism for strategic selection of highly-confident target samples essential for the estimation of class-importance weights. Furthermore, we capture class-discriminative and domain-invariant features by coupling the process of achieving compact and distinct class distributions with an adversarial objective. Experimental findings over numerous cross-domain classification tasks demonstrate the potential of the proposed technique to deliver superior and comparable accuracy over existing methods.