44,774 research outputs found
Multi-task CNN Model for Attribute Prediction
This paper proposes a joint multi-task learning algorithm to better predict
attributes in images using deep convolutional neural networks (CNN). We
consider learning binary semantic attributes through a multi-task CNN model,
where each CNN will predict one binary attribute. The multi-task learning
allows CNN models to simultaneously share visual knowledge among different
attribute categories. Each CNN will generate attribute-specific feature
representations, and then we apply multi-task learning on the features to
predict their attributes. In our multi-task framework, we propose a method to
decompose the overall model's parameters into a latent task matrix and
combination matrix. Furthermore, under-sampled classifiers can leverage shared
statistics from other classifiers to improve their performance. Natural
grouping of attributes is applied such that attributes in the same group are
encouraged to share more knowledge. Meanwhile, attributes in different groups
will generally compete with each other, and consequently share less knowledge.
We show the effectiveness of our method on two popular attribute datasets.Comment: 11 pages, 3 figures, ieee transaction pape
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing
With the aim of developing a fast yet accurate algorithm for compressive
sensing (CS) reconstruction of natural images, we combine in this paper the
merits of two existing categories of CS methods: the structure insights of
traditional optimization-based methods and the speed of recent network-based
ones. Specifically, we propose a novel structured deep network, dubbed
ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm
(ISTA) for optimizing a general norm CS reconstruction model. To cast
ISTA into deep network form, we develop an effective strategy to solve the
proximal mapping associated with the sparsity-inducing regularizer using
nonlinear transforms. All the parameters in ISTA-Net (\eg nonlinear transforms,
shrinkage thresholds, step sizes, etc.) are learned end-to-end, rather than
being hand-crafted. Moreover, considering that the residuals of natural images
are more compressible, an enhanced version of ISTA-Net in the residual domain,
dubbed {ISTA-Net}, is derived to further improve CS reconstruction.
Extensive CS experiments demonstrate that the proposed ISTA-Nets outperform
existing state-of-the-art optimization-based and network-based CS methods by
large margins, while maintaining fast computational speed. Our source codes are
available: \textsl{http://jianzhang.tech/projects/ISTA-Net}.Comment: 10 pages, 6 figures, 4 Tables. To appear in CVPR 201
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