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Network In Network
TLDR
With enhanced local modeling via the micro network, the proposed deep network structure NIN is able to utilize global average pooling over feature maps in the classification layer, which is easier to interpret and less prone to overfitting than traditional fully connected layers.
Robust Recovery of Subspace Structures by Low-Rank Representation
TLDR
It is shown that the convex program associated with LRR solves the subspace clustering problem in the following sense: When the data is clean, LRR exactly recovers the true subspace structures; when the data are contaminated by outliers, it is proved that under certain conditions LRR can exactly recover the row space of the original data.
Face recognition using Laplacianfaces
TLDR
Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
TLDR
A new supervised dimensionality reduction algorithm called marginal Fisher analysis is proposed in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizing the interclass separability.
Neighborhood preserving embedding
TLDR
This paper proposes a novel subspace learning algorithm called neighborhood preserving embedding (NPE), which aims at preserving the local neighborhood structure on the data manifold and is less sensitive to outliers than principal component analysis (PCA).
Deep Joint Rain Detection and Removal from a Single Image
TLDR
A recurrent rain detection and removal network that removes rain streaks and clears up the rain accumulation iteratively and progressively is proposed and a new contextualized dilated network is developed to exploit regional contextual information and to produce better representations for rain detection.
An HOG-LBP human detector with partial occlusion handling
TLDR
By combining Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP) as the feature set, this work proposes a novel human detection approach capable of handling partial occlusion and achieves the best human detection performance on the INRIA dataset.
Supervised Hashing for Image Retrieval via Image Representation Learning
TLDR
Extensive empirical evaluations on three benchmark datasets with different kinds of images show that the proposed method has superior performance gains over several state-of-the-art supervised and unsupervised hashing methods.
Simultaneous feature learning and hash coding with deep neural networks
TLDR
Extensive evaluations on several benchmark image datasets show that the proposed simultaneous feature learning and hash coding pipeline brings substantial improvements over other state-of-the-art supervised or unsupervised hashing methods.
Robust and Efficient Subspace Segmentation via Least Squares Regression
TLDR
This paper presents the Least Squares Regression (LSR) method for subspace segmentation, which takes advantage of data correlation, which is common in real data and significantly outperforms state-of-the-art methods.
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