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Found 23 papers, 7 papers with code
A Scale Invariant Flatness Measure for Deep Network Minima
It has been empirically observed that the flatness of minima obtained from training deep networks seems to correlate with better generalization.
Reducing Sampling Ratios Improves Bagging in Sparse Regression
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.
Generative Adversarial Networks for Recovering Missing Spectral Information
Ultra-wideband (UWB) radar systems nowadays typical operate in the low frequency spectrum to achieve penetration capability.
JOBS: Joint-Sparse Optimization from Bootstrap Samples
In practice, it is often the case that not all measurements are available or required for recovery.
Adversarial Deep Structured Nets for Mass Segmentation from Mammograms
Mass segmentation provides effective morphological features which are important for mass diagnosis.
Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
In this paper, we propose an automatic and fast algorithm to localize and label the vertebra centroids in 3D CT volumes.
Regularizing Face Verification Nets For Pain Intensity Regression
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.
Supervised Deep Sparse Coding Networks
In this paper, we describe the deep sparse coding network (SCN), a novel deep network that encodes intermediate representations with nonnegative sparse coding.
Linear Disentangled Representation Learning for Facial Actions
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.
Adversarial Deep Structural Networks for Mammographic Mass Segmentation
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.
Pose-Selective Max Pooling for Measuring Similarity
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.
Detecting Burnscar from Hyperspectral Imagery via Sparse Representation with Low-Rank Interference
In this paper, we propose a burnscar detection model for hyperspectral imaging (HSI) data.
Sparse Coding with Fast Image Alignment via Large Displacement Optical Flow
Sparse representation-based classifiers have shown outstanding accuracy and robustness in image classification tasks even with the presence of intense noise and occlusion.
ICR: Iterative Convex Refinement for Sparse Signal Recovery Using Spike and Slab Priors
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.
Task-Driven Dictionary Learning for Hyperspectral Image Classification with Structured Sparsity Constraints
We propose to enforce structured sparsity priors on the task-driven dictionary learning method in order to improve the performance of the hyperspectral classification.
Multi-task Image Classification via Collaborative, Hierarchical Spike-and-Slab Priors
Promising results have been achieved in image classification problems by exploiting the discriminative power of sparse representations for classification (SRC).
Collaborative Multi-sensor Classification via Sparsity-based Representation
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.
Hierarchical Sparse and Collaborative Low-Rank Representation for Emotion Recognition
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.
Structured Dictionary Learning for Classification
Sparsity driven signal processing has gained tremendous popularity in the last decade.
Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification
Pixel-wise classification, where each pixel is assigned to a predefined class, is one of the most important procedures in hyperspectral image (HSI) analysis.
Robust Lasso with missing and grossly corrupted observations
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.
Discriminative Local Sparse Representations for Robust Face Recognition
We propose a probabilistic graphical model framework to explicitly mine the conditional dependencies between these distinct sparse local features.
