A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation

By Jian Liang, Yunbo Wang, Dapeng Hu, Ran He, Jiashi Feng. 2020

123 citations recorded by the source corpus.

This work addresses the unsupervised domain adaptation problem, especially in the case of class labels in the target domain being only a subset of those in the source domain. Such a partial transfer setting is realistic but challenging and existing methods always suffer from two key problems, negative transfer and uncertainty propagation. In this paper, we build on domain adversarial learning and propose a novel domain adaptation method BA$^3$US with two new techniques termed Balanced Adversarial Alignment (BAA) and Adaptive Uncertainty Suppression (AUS), respectively. On one hand, negative transfer results in misclassification of target samples to the classes only present in the source domain. To address this issue, BAA pursues the balance between label distributions across domains in a fairly simple manner. Specifically, it randomly leverages a few source samples to augment the smaller target domain during domain alignment so that classes in different domains are symmetric. On the other hand, a source sample would be denoted as uncertain if there is an incorrect class that has a relatively high prediction score, and such uncertainty easily propagates to unlabeled target data around it during alignment, which severely deteriorates adaptation performance. Thus we present AUS that emphasizes uncertain samples and exploits an adaptive weighted complement entropy objective to encourage incorrect classes to have uniform and low prediction scores. Experimental results on multiple benchmarks demonstrate our BA$^3$US surpasses state-of-the-arts for partial domain adaptation tasks. Code is available at \url{this https URL}.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mingkui Tan
2. Jiashi Feng
3. Yunchao Wei
4. Yifan Zhang
5. Chuang Gan
6. Yi Yang
7. Jian Liang
8. Shuaicheng Niu

In Computer Vision and Pattern Recognition

1. Jiashi Feng
2. Yi Yang
3. Yunchao Wei
4. Mingkui Tan
5. Luc Van Gool
6. Chuang Gan
7. Trevor Darrell
8. Dacheng Tao

Citations in the corpus

• Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation (2022, methodology): “Step 3 [20,21,22,23,24,25,26,27,28,29] Postit Notes, Mug, Webcam, Desk Lamp, Telephone, Helmet, Mouse, Pen, Monitor, Mop”
• Domain Consensus Clustering for Universal Domain Adaptation (2021, background): “Recently, ETN [5] proposes a progressive weighting scheme to estimate the transferability of source samples, while BA3US [36] incorporates an augmentation scheme and a complement entropy to avoid negative transfer and uncertainty propagation, respectively.”
• A Comprehensive Survey on Test-Time Adaptation Under Distribution Shifts (2023, unknown): “Normalization layers ( e.g. , batch normalization [233] and layer normalization [234]) are considered essential components of modern neural networks.”
• Source Data-Absent Unsupervised Domain Adaptation Through Hypothesis Transfer and Labeling Transfer (2020, methodology): “For partial-set DA tasks, we compare ours with IWAN [105], SAN [91], ETN [106], DRCN [107], RTNetadv [108], BA(3)US [109], and TSCDA [110].”