1,868 research outputs found
Efficient Continuous Manifold Learning for Time Series Modeling
Modeling non-Euclidean data is drawing attention along with the unprecedented
successes of deep neural networks in diverse fields. In particular, symmetric
positive definite (SPD) matrix is being actively studied in computer vision,
signal processing, and medical image analysis, thanks to its ability to learn
appropriate statistical representations. However, due to its strong
constraints, it remains challenging for optimization problems or inefficient
computation costs, especially, within a deep learning framework. In this paper,
we propose to exploit a diffeomorphism mapping between Riemannian manifolds and
a Cholesky space, by which it becomes feasible not only to efficiently solve
optimization problems but also to reduce computation costs greatly. Further, in
order for dynamics modeling in time series data, we devise a continuous
manifold learning method by integrating a manifold ordinary differential
equation and a gated recurrent neural network in a systematic manner. It is
noteworthy that because of the nice parameterization of matrices in a Cholesky
space, it is straightforward to train our proposed network with Riemannian
geometric metrics equipped. We demonstrate through experiments that the
proposed model can be efficiently and reliably trained as well as outperform
existing manifold methods and state-of-the-art methods in two classification
tasks: action recognition and sleep staging classification
Manifold-valued Image Generation with Wasserstein Generative Adversarial Nets
Generative modeling over natural images is one of the most fundamental
machine learning problems. However, few modern generative models, including
Wasserstein Generative Adversarial Nets (WGANs), are studied on manifold-valued
images that are frequently encountered in real-world applications. To fill the
gap, this paper first formulates the problem of generating manifold-valued
images and exploits three typical instances: hue-saturation-value (HSV) color
image generation, chromaticity-brightness (CB) color image generation, and
diffusion-tensor (DT) image generation. For the proposed generative modeling
problem, we then introduce a theorem of optimal transport to derive a new
Wasserstein distance of data distributions on complete manifolds, enabling us
to achieve a tractable objective under the WGAN framework. In addition, we
recommend three benchmark datasets that are CIFAR-10 HSV/CB color images,
ImageNet HSV/CB color images, UCL DT image datasets. On the three datasets, we
experimentally demonstrate the proposed manifold-aware WGAN model can generate
more plausible manifold-valued images than its competitors.Comment: Accepted by AAAI 201
- …