3,568 research outputs found
Deep Mean-Shift Priors for Image Restoration
In this paper we introduce a natural image prior that directly represents a
Gaussian-smoothed version of the natural image distribution. We include our
prior in a formulation of image restoration as a Bayes estimator that also
allows us to solve noise-blind image restoration problems. We show that the
gradient of our prior corresponds to the mean-shift vector on the natural image
distribution. In addition, we learn the mean-shift vector field using denoising
autoencoders, and use it in a gradient descent approach to perform Bayes risk
minimization. We demonstrate competitive results for noise-blind deblurring,
super-resolution, and demosaicing.Comment: NIPS 201
Challenges in Disentangling Independent Factors of Variation
We study the problem of building models that disentangle independent factors
of variation. Such models could be used to encode features that can efficiently
be used for classification and to transfer attributes between different images
in image synthesis. As data we use a weakly labeled training set. Our weak
labels indicate what single factor has changed between two data samples,
although the relative value of the change is unknown. This labeling is of
particular interest as it may be readily available without annotation costs. To
make use of weak labels we introduce an autoencoder model and train it through
constraints on image pairs and triplets. We formally prove that without
additional knowledge there is no guarantee that two images with the same factor
of variation will be mapped to the same feature. We call this issue the
reference ambiguity. Moreover, we show the role of the feature dimensionality
and adversarial training. We demonstrate experimentally that the proposed model
can successfully transfer attributes on several datasets, but show also cases
when the reference ambiguity occurs.Comment: Submitted to ICLR 201
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