no code implementations • NeurIPS 2019 • Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi, Nhung Ngo, Thi Hoai Phuong Nguyen, Zhongyu Lou, Thomas Brox
Alternative unsupervised approaches rely on careful selection of multiple handcrafted saliency methods to generate noisy pseudo-ground-truth labels.
no code implementations • 28 Sep 2019 • Duc Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi, Thi Phuong Nhung Ngo, Thi Hoai Phuong Nguyen, Zhongyu Lou, Thomas Brox
Alternative unsupervised approaches rely on careful selection of multiple handcrafted saliency methods to generate noisy pseudo-ground-truth labels.
no code implementations • 1 Jun 2019 • Duc Tam Nguyen, Thi-Phuong-Nhung Ngo, Zhongyu Lou, Michael Klar, Laura Beggel, Thomas Brox
We consider the problem of training a model under the presence of label noise.
2 code implementations • ICLR 2019 • Duc Tam Nguyen, Zhongyu Lou, Michael Klar, Thomas Brox
Thus, due to the lack of representative data, the wide-spread discriminative approaches cannot cover such learning tasks, and rather generative models, which attempt to learn the input density of the normal cases, are used.
2 code implementations • ICLR 2019 • Duc Tam Nguyen, Zhongyu Lou, Michael Klar, Thomas Brox
In one-class-learning tasks, only the normal case (foreground) can be modeled with data, whereas the variation of all possible anomalies is too erratic to be described by samples.
3 code implementations • CVPR 2016 • Jörn-Henrik Jacobsen, Jan van Gemert, Zhongyu Lou, Arnold W. M. Smeulders
We combine these ideas into structured receptive field networks, a model which has a fixed filter basis and yet retains the flexibility of CNNs.
no code implementations • 6 Mar 2015 • Ninghang Hu, Gwenn Englebienne, Zhongyu Lou, Ben Kröse
The model is embedded with a latent layer that is able to capture a richer class of contextual information in both state-state and observation-state pairs.