Add to ORKGAljundi R., Tuytelaars T., ''Lightweight unsupervised domain adaptation by convolutional filter reconstruction'', Workshop on TASK-CV: Transferring and Adapting Source Knowledge in Computer Vision, in conjunction with ECCV 2016, 7 pp., October 9, 2016, Amsterdam, The Netherlands.status: publishe
This thesis addresses a critical problem in computer vision of dealing with dataset bias between sou...
As of today, object categorization algorithms are not able to achieve the level of robustness and ge...
Images seen during test time are often not from the same distribution as images used for learning. T...
Unsupervised domain adaptation involves knowledge transfer from a labeled source to unlabeled target...
Current Domain Adaptation (DA) methods based on deep architectures assume that the source samples ar...
© 2018, Springer Nature Switzerland AG. In this paper, we make two contributions to unsupervised dom...
The performance of a classifier trained on data coming from a specific domain typically degrades whe...
Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain wh...
One of the main limitations of artificial intelligence today is its inability to adapt to unforeseen...
Artificial intelligent and machine learning technologies have already achieved significant success i...
In the presence of large sets of labeled data, Deep Learning DL has accomplished extraordinary trium...
Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-i...
Recent studies reveal that a deep neural network can learn transferable features which generalize we...
The advent of deep convolutional networks has powered a new wave of progress in visual recognition. ...
Most machine learning algorithms assume that training and test data are sampled from the same distri...
This thesis addresses a critical problem in computer vision of dealing with dataset bias between sou...
As of today, object categorization algorithms are not able to achieve the level of robustness and ge...
Images seen during test time are often not from the same distribution as images used for learning. T...
Unsupervised domain adaptation involves knowledge transfer from a labeled source to unlabeled target...
Current Domain Adaptation (DA) methods based on deep architectures assume that the source samples ar...
© 2018, Springer Nature Switzerland AG. In this paper, we make two contributions to unsupervised dom...
The performance of a classifier trained on data coming from a specific domain typically degrades whe...
Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain wh...
One of the main limitations of artificial intelligence today is its inability to adapt to unforeseen...
Artificial intelligent and machine learning technologies have already achieved significant success i...
In the presence of large sets of labeled data, Deep Learning DL has accomplished extraordinary trium...
Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-i...
Recent studies reveal that a deep neural network can learn transferable features which generalize we...
The advent of deep convolutional networks has powered a new wave of progress in visual recognition. ...
Most machine learning algorithms assume that training and test data are sampled from the same distri...
This thesis addresses a critical problem in computer vision of dealing with dataset bias between sou...
As of today, object categorization algorithms are not able to achieve the level of robustness and ge...
Images seen during test time are often not from the same distribution as images used for learning. T...