Deep visual domain adaptation: A survey

Wang, Mei; Deng, Weihong

doi:10.1016/j.neucom.2018.05.083

Cited by 1,854 publications

(975 citation statements)

References 148 publications

(320 reference statements)

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“…Similar improvement can be observed in the MSD and HD metric. We want to emphasize that our networks are trained separately on each dataset and are completely unaware of the new data distribution, unlike a typical domain adaptation setting. Nonetheless, the distance map regularized networks are able to generalize better to a new dataset compared to the baseline models.…”

Section: Resultsmentioning

confidence: 99%

A distance map regularized CNN for cardiac cine MR image segmentation

2019

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Purpose Cardiac image segmentation is a critical process for generating personalized models of the heart and for quantifying cardiac performance parameters. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV), and the myocardium from cardiac cine MR images is challenging due to variability of the normal and abnormal anatomy, as well as the imaging protocols. This study proposes a multi‐task learning (MTL)‐based regularization of a convolutional neural network (CNN) to obtain accurate segmenation of the cardiac structures from cine MR images. Methods We train a CNN network to perform the main task of semantic segmentation, along with the simultaneous, auxiliary task of pixel‐wise distance map regression. The network also predicts uncertainties associated with both tasks, such that their losses are weighted by the inverse of their corresponding uncertainties. As a result, during training, the task featuring a higher uncertainty is weighted less and vice versa. The proposed distance map regularizer is a decoder network added to the bottleneck layer of an existing CNN architecture, facilitating the network to learn robust global features. The regularizer block is removed after training, so that the original number of network parameters does not change. The trained network outputs per‐pixel segmentation when a new patient cine MR image is provided as an input. Results We show that the proposed regularization method improves both binary and multi‐class segmentation performance over the corresponding state‐of‐the‐art CNN architectures. The evaluation was conducted on two publicly available cardiac cine MRI datasets, yielding average Dice coefficients of 0.84 ± 0.03 and 0.91 ± 0.04. We also demonstrate improved generalization performance of the distance map regularized network on cross‐dataset segmentation, showing as much as 42% improvement in myocardium Dice coefficient from 0.56 ± 0.28 to 0.80 ± 0.14. Conclusions We have presented a method for accurate segmentation of cardiac structures from cine MR images. Our experiments verify that the proposed method exceeds the segmentation performance of three existing state‐of‐the‐art methods. Furthermore, several cardiac indices that often serve as diagnostic biomarkers, specifically blood pool volume, myocardial mass, and ejection fraction, computed using our method are better correlated with the indices computed from the reference, ground truth segmentation. Hence, the proposed method has the potential to become a non‐invasive screening and diagnostic tool for the clinical assessment of various cardiac conditions, as well as a reliable aid for generating patient specific models of the cardiac anatomy for therapy planning, simulation, and guidance.

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Section: Resultsmentioning

confidence: 99%

A distance map regularized CNN for cardiac cine MR image segmentation

2019

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“…We present an experimental analysis on the historical logs of a major display advertising platform 4 . Specifically, we evaluate our approaches across 149 partners, at different points of their campaign, i.e., we experiment with varying amounts of available data for the partners.…”

Section: Introductionmentioning

confidence: 99%

“…Experimental results show that the proposed approaches outperform the baselines approaches at all points of the campaign, with LADA performing the best. Additionally, we also perform an extensive analysis of the proposed approaches on the special case of partner cold-start, 4 https://www.criteo.com/ i.e., when no historical data is available for a partner, and show the advantage of the proposed approaches over the competing approaches. For example, on the Mean Average Precision metric, LADA and IADA outperform the non-domain-adaptive baseline by 8.574% and 2.710% at cold-start, respectively.…”

Section: Introductionmentioning

confidence: 99%

Targeted display advertising: the case of preferential attachment

Manchanda

Yadav²,

Doan

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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An average adult is exposed to hundreds of digital advertisements daily 1 , making the digital advertisement industry a classic example of a big-data-driven platform. As such, the ad-tech industry relies on historical engagement logs (clicks or purchases) to identify potentially interested users for the advertisement campaign of a partner (a seller who wants to target users for its products). The number of advertisements that are shown for a partner, and hence the historical campaign data available for a partner depends upon the budget constraints of the partner. Thus, enough data can be collected for the high-budget partners to make accurate predictions, while this is not the case with the low-budget partners. This skewed distribution of the data leads to preferential attachment of the targeted display advertising platforms towards the high-budget partners. In this paper, we develop domain-adaptation approaches to address the challenge of predicting interested users for the partners with insufficient data, i.e., the tail partners. Specifically, we develop simple yet effective approaches that leverage the similarity among the partners to transfer information from the partners with sufficient data to cold-start partners, i.e., partners without any campaign data. Our approaches readily adapt to the new campaign data by incremental fine-tuning, and hence work at varying points of a campaign, and not just the coldstart. We present an experimental analysis on the historical logs of a major display advertising platform 2 . Specifically, we evaluate our approaches across 149 partners, at varying points of their campaigns. Experimental results show that the proposed approaches outperform the other domain-adaptation approaches at different time points of the campaigns.

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“…Methods include getting more data (e.g. multimodal learning [3], domain adaptation [30] or data augmentation, depending on the available resources) or learning from data on similar problems.…”

Section: Introductionmentioning

confidence: 99%

Picking Groups Instead of Samples: A Close Look at Static Pool-Based Meta-Active Learning

Mas

Morros

2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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Active Learning techniques are used to tackle learning problems where obtaining training labels is costly. In this work we use Meta-Active Learning to learn to select a subset of samples from a pool of unsupervised input for further annotation. This scenario is called Static Pool-based Meta-Active Learning. We propose to extend existing approaches by performing the selection in a manner that, unlike previous works, can handle the selection of each sample based on the whole selected subset.

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Deep visual domain adaptation: A survey

Cited by 1,854 publications

References 148 publications

A distance map regularized CNN for cardiac cine MR image segmentation

A distance map regularized CNN for cardiac cine MR image segmentation

Targeted display advertising: the case of preferential attachment

Picking Groups Instead of Samples: A Close Look at Static Pool-Based Meta-Active Learning

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