28,522 research outputs found
Self-Supervised Variational Auto-Encoders
Density estimation, compression and data generation are crucial tasks in
artificial intelligence. Variational Auto-Encoders (VAEs) constitute a single
framework to achieve these goals. Here, we present a novel class of generative
models, called self-supervised Variational Auto-Encoder (selfVAE), that
utilizes deterministic and discrete variational posteriors. This class of
models allows to perform both conditional and unconditional sampling, while
simplifying the objective function. First, we use a single self-supervised
transformation as a latent variable, where a transformation is either
downscaling or edge detection. Next, we consider a hierarchical architecture,
i.e., multiple transformations, and we show its benefits compared to the VAE.
The flexibility of selfVAE in data reconstruction finds a particularly
interesting use case in data compression tasks, where we can trade-off memory
for better data quality, and vice-versa. We present performance of our approach
on three benchmark image data (Cifar10, Imagenette64, and CelebA).Comment: 19 pages, 14 figures, 2 table
Geometrical-based algorithm for variational segmentation and smoothing of vector-valued images
An optimisation method based on a nonlinear functional is considered for segmentation and smoothing of vector-valued images. An edge-based approach is proposed to initially segment the image using geometrical properties such as metric tensor of the linearly smoothed image. The nonlinear functional is then minimised for each segmented region to yield the smoothed image. The functional is characterised with a unique solution in contrast with the MumfordâShah functional for vector-valued images. An operator for edge detection is introduced as a result of this unique solution. This operator is analytically calculated and its detection performance and localisation are then compared with those of the DroGoperator. The implementations are applied on colour images as examples of vector-valued images, and the results demonstrate robust performance in noisy environments
A variational algorithm for the detection of line segments
In this paper we propose an algorithm for the detection of edges in images
that is based on topological asymptotic analysis. Motivated from the
Mumford--Shah functional, we consider a variational functional that penalizes
oscillations outside some approximate edge set, which we represent as the union
of a finite number of thin strips, the width of which is an order of magnitude
smaller than their length. In order to find a near optimal placement of these
strips, we compute an asymptotic expansion of the functional with respect to
the strip size. This expansion is then employed for defining a (topological)
gradient descent like minimization method. As opposed to a recently proposed
method by some of the authors, which uses coverings with balls, the usage of
strips includes some directional information into the method, which can be used
for obtaining finer edges and can also result in a reduction of computation
times
Modeling heterogeneity in random graphs through latent space models: a selective review
We present a selective review on probabilistic modeling of heterogeneity in
random graphs. We focus on latent space models and more particularly on
stochastic block models and their extensions that have undergone major
developments in the last five years
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
While most scene flow methods use either variational optimization or a strong
rigid motion assumption, we show for the first time that scene flow can also be
estimated by dense interpolation of sparse matches. To this end, we find sparse
matches across two stereo image pairs that are detected without any prior
regularization and perform dense interpolation preserving geometric and motion
boundaries by using edge information. A few iterations of variational energy
minimization are performed to refine our results, which are thoroughly
evaluated on the KITTI benchmark and additionally compared to state-of-the-art
on MPI Sintel. For application in an automotive context, we further show that
an optional ego-motion model helps to boost performance and blends smoothly
into our approach to produce a segmentation of the scene into static and
dynamic parts.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
201
Multistart Methods for Quantum Approximate Optimization
Hybrid quantum-classical algorithms such as the quantum approximate
optimization algorithm (QAOA) are considered one of the most promising
approaches for leveraging near-term quantum computers for practical
applications. Such algorithms are often implemented in a variational form,
combining classical optimization methods with a quantum machine to find
parameters to maximize performance. The quality of the QAOA solution depends
heavily on quality of the parameters produced by the classical optimizer.
Moreover, the presence of multiple local optima in the space of parameters
makes it harder for the classical optimizer. In this paper we study the use of
a multistart optimization approach within a QAOA framework to improve the
performance of quantum machines on important graph clustering problems. We also
demonstrate that reusing the optimal parameters from similar problems can
improve the performance of classical optimization methods, expanding on similar
results for MAXCUT
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