no code implementations • 6 Feb 2019 • Akshay Rangamani, Nam H. Nguyen, Abhishek Kumar, Dzung Phan, Sang H. Chin, Trac. D. Tran
It has been empirically observed that the flatness of minima obtained from training deep networks seems to correlate with better generalization.
no code implementations • 20 Dec 2018 • Luoluo Liu, Sang Peter Chin, Trac. D. Tran
With a properly chosen sampling ratio, a reasonably small number of estimates K = 30 gives satisfying result, even though increasing K is discovered to always improve or at least maintain the performance.
no code implementations • 11 Dec 2018 • Dung N. Tran, Trac. D. Tran, Lam Nguyen
Ultra-wideband (UWB) radar systems nowadays typical operate in the low frequency spectrum to achieve penetration capability.
no code implementations • 8 Oct 2018 • Luoluo Liu, Sang Peter Chin, Trac. D. Tran
In practice, it is often the case that not all measurements are available or required for recovery.
1 code implementation • 24 Oct 2017 • Wentao Zhu, Xiang Xiang, Trac. D. Tran, Gregory D. Hager, Xiaohui Xie
Mass segmentation provides effective morphological features which are important for mass diagnosis.
no code implementations • 12 Aug 2017 • Akshay Rangamani, Anirbit Mukherjee, Amitabh Basu, Tejaswini Ganapathy, Ashish Arora, Sang Chin, Trac. D. Tran
This property holds independent of the loss function.
no code implementations • 17 May 2017 • Dong Yang, Tao Xiong, Daguang Xu, Qiangui Huang, David Liu, S. Kevin Zhou, Zhoubing Xu, Jin-Hyeong Park, Mingqing Chen, Trac. D. Tran, Sang Peter Chin, Dimitris Metaxas, Dorin Comaniciu
In this paper, we propose an automatic and fast algorithm to localize and label the vertebra centroids in 3D CT volumes.
2 code implementations • 22 Feb 2017 • Feng Wang, Xiang Xiang, Chang Liu, Trac. D. Tran, Austin Reiter, Gregory D. Hager, Harry Quon, Jian Cheng, Alan L. Yuille
In this way, the expression intensity regression task can benefit from the rich feature representations trained on a huge amount of data for face verification.
1 code implementation • 29 Jan 2017 • Xiaoxia Sun, Nasser M. Nasrabadi, Trac. D. Tran
In this paper, we describe the deep sparse coding network (SCN), a novel deep network that encodes intermediate representations with nonnegative sparse coding.
2 code implementations • 11 Jan 2017 • Xiang Xiang, Trac. D. Tran
Limited annotated data available for the recognition of facial expression and action units embarrasses the training of deep networks, which can learn disentangled invariant features.
1 code implementation • 18 Dec 2016 • Wentao Zhu, Xiang Xiang, Trac. D. Tran, Xiaohui Xie
Experimental results on two public datasets, INbreast and DDSM-BCRP, show that our end-to-end network combined with adversarial training achieves the-state-of-the-art results.
2 code implementations • 22 Sep 2016 • Xiang Xiang, Trac. D. Tran
In this paper, we deal with two challenges for measuring the similarity of the subject identities in practical video-based face recognition - the variation of the head pose in uncontrolled environments and the computational expense of processing videos.
no code implementations • 1 May 2016 • Minh Dao, Xiang Xiang, Bulent Ayhan, Chiman Kwan, Trac. D. Tran
In this paper, we propose a burnscar detection model for hyperspectral imaging (HSI) data.
no code implementations • 21 Dec 2015 • Xiaoxia Sun, Nasser M. Nasrabadi, Trac. D. Tran
Sparse representation-based classifiers have shown outstanding accuracy and robustness in image classification tasks even with the presence of intense noise and occlusion.
no code implementations • 16 Feb 2015 • Hojjat S. Mousavi, Vishal Monga, Trac. D. Tran
Essentially, ICR solves a sequence of convex optimization problems such that sequence of solutions converges to a sub-optimal solution of the original hard optimization problem.
no code implementations • 3 Feb 2015 • Xiaoxia Sun, Nasser M. Nasrabadi, Trac. D. Tran
We propose to enforce structured sparsity priors on the task-driven dictionary learning method in order to improve the performance of the hyperspectral classification.
no code implementations • 30 Jan 2015 • Hojjat Seyed Mousavi, Umamahesh Srinivas, Vishal Monga, Yuanming Suo, Minh Dao, Trac. D. Tran
Promising results have been achieved in image classification problems by exploiting the discriminative power of sparse representations for classification (SRC).
no code implementations • 29 Oct 2014 • Minh Dao, Nam H. Nguyen, Nasser M. Nasrabadi, Trac. D. Tran
In this paper, we propose a general collaborative sparse representation framework for multi-sensor classification, which takes into account the correlations as well as complementary information between heterogeneous sensors simultaneously while considering joint sparsity within each sensor's observations.
1 code implementation • 7 Oct 2014 • Xiang Xiang, Minh Dao, Gregory D. Hager, Trac. D. Tran
In this paper, we design a Collaborative-Hierarchical Sparse and Low-Rank (C-HiSLR) model that is natural for recognizing human emotion in visual data.
no code implementations • 8 Jun 2014 • Yuanming Suo, Minh Dao, Umamahesh Srinivas, Vishal Monga, Trac. D. Tran
Sparsity driven signal processing has gained tremendous popularity in the last decade.
no code implementations • 16 Jan 2014 • Xiaoxia Sun, Qing Qu, Nasser M. Nasrabadi, Trac. D. Tran
Pixel-wise classification, where each pixel is assigned to a predefined class, is one of the most important procedures in hyperspectral image (HSI) analysis.
no code implementations • NeurIPS 2011 • Nasser M. Nasrabadi, Trac. D. Tran, Nam Nguyen
Our second set of results applies to a general class of Gaussian design matrix $X$ with i. i. d rows $\oper N(0, \Sigma)$, for which we provide a surprising phenomenon: the extended Lasso can recover exact signed supports of both $\beta^{\star}$ and $e^{\star}$ from only $\Omega(k \log p \log n)$ observations, even the fraction of corruption is arbitrarily close to one.
no code implementations • 8 Nov 2011 • Yi Chen, Umamahesh Srinivas, Thong T. Do, Vishal Monga, Trac. D. Tran
We propose a probabilistic graphical model framework to explicitly mine the conditional dependencies between these distinct sparse local features.