2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2019
DOI: 10.1109/cvpr.2019.00234
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Transferrable Prototypical Networks for Unsupervised Domain Adaptation

Abstract: In this paper, we introduce a new idea for unsupervised domain adaptation via a remold of Prototypical Networks, which learn an embedding space and perform classification via a remold of the distances to the prototype of each class. Specifically, we present Transferrable Prototypical Networks (TPN) for adaptation such that the prototypes for each class in source and target domains are close in the embedding space and the score distributions predicted by prototypes separately on source and target data are simil… Show more

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Cited by 326 publications
(219 citation statements)
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“…Compared with the semantic domain alignment methods (TPN [12], DIAL [14], SCA [15]) and the target classifier learning methods (iCAN [17]), our method outperforms them in most transfer tasks by jointly optimizing semantic domain alignment and target classifier learning in the feature space. Compared with state-of-the-art methods, SDA-TCL achieves better or comparable performance in all transfer tasks.…”
Section: Resultsmentioning
confidence: 99%
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“…Compared with the semantic domain alignment methods (TPN [12], DIAL [14], SCA [15]) and the target classifier learning methods (iCAN [17]), our method outperforms them in most transfer tasks by jointly optimizing semantic domain alignment and target classifier learning in the feature space. Compared with state-of-the-art methods, SDA-TCL achieves better or comparable performance in all transfer tasks.…”
Section: Resultsmentioning
confidence: 99%
“…On par with these methods aligning distributions in the feature space, some methods align distributions in raw pixel space by translating source data to the target domain with Image to Image translation techniques [25][26][27][28][29][30]. In addition to domain-level distribution alignment, the class-level information in target data is also frequently used to align class-level distributions [11][12][13][14][15]31]. Compared with these methods, our method not only aligns class-level distributions, but also learns target discriminative features.…”
Section: Related Workmentioning
confidence: 99%
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“…Therefore, a well-trained model on the GCC dataset doesn't work well in the real world. Some of the recent work [17][18][19][20][21] provided us with a domain adaptation strategy, using image style transfer networks to narrow the domain gap. For the cross-domain crowd counting problem, [16] also proposed a domain adaptation method to make synthetic data closer to real data in visual perception via the SE Cycle GAN [16] network.…”
Section: Introductionmentioning
confidence: 99%
“…A neural network optimization algorithm of finding the loss of each round of neural network generation is a core feature of few-shot learning (Garcia, V. and Joan B., 2017). The loss algorithm applied is based on a prototypical neural network with adjustment of using accuracy rate instead of Euclidean distance; since among the various neural networks available, a prototypical neural network is the most reliable means of approach in this situation thanks to its outstanding performance in the small sample space in practices (Pan, Y. et al, 2019), which often outputs the prediction accuracy that has surpassed human recognition (He, K. et al, 2016). By integrating with few-shot learning algorithms, the prototypical neural network achieved an approximated 70% accuracy in 5-way 5-shot image classification (Richard, Z. et al, 2017).…”
Section: Introductionmentioning
confidence: 99%