5,312 research outputs found
Hyperbolic Interaction Model For Hierarchical Multi-Label Classification
Different from the traditional classification tasks which assume mutual
exclusion of labels, hierarchical multi-label classification (HMLC) aims to
assign multiple labels to every instance with the labels organized under
hierarchical relations. Besides the labels, since linguistic ontologies are
intrinsic hierarchies, the conceptual relations between words can also form
hierarchical structures. Thus it can be a challenge to learn mappings from word
hierarchies to label hierarchies. We propose to model the word and label
hierarchies by embedding them jointly in the hyperbolic space. The main reason
is that the tree-likeness of the hyperbolic space matches the complexity of
symbolic data with hierarchical structures. A new Hyperbolic Interaction Model
(HyperIM) is designed to learn the label-aware document representations and
make predictions for HMLC. Extensive experiments are conducted on three
benchmark datasets. The results have demonstrated that the new model can
realistically capture the complex data structures and further improve the
performance for HMLC comparing with the state-of-the-art methods. To facilitate
future research, our code is publicly available
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction
Local learning of sparse image models has proven to be very effective to
solve inverse problems in many computer vision applications. To learn such
models, the data samples are often clustered using the K-means algorithm with
the Euclidean distance as a dissimilarity metric. However, the Euclidean
distance may not always be a good dissimilarity measure for comparing data
samples lying on a manifold. In this paper, we propose two algorithms for
determining a local subset of training samples from which a good local model
can be computed for reconstructing a given input test sample, where we take
into account the underlying geometry of the data. The first algorithm, called
Adaptive Geometry-driven Nearest Neighbor search (AGNN), is an adaptive scheme
which can be seen as an out-of-sample extension of the replicator graph
clustering method for local model learning. The second method, called
Geometry-driven Overlapping Clusters (GOC), is a less complex nonadaptive
alternative for training subset selection. The proposed AGNN and GOC methods
are evaluated in image super-resolution, deblurring and denoising applications
and shown to outperform spectral clustering, soft clustering, and geodesic
distance based subset selection in most settings.Comment: 15 pages, 10 figures and 5 table
3D Point Cloud Denoising via Deep Neural Network based Local Surface Estimation
We present a neural-network-based architecture for 3D point cloud denoising
called neural projection denoising (NPD). In our previous work, we proposed a
two-stage denoising algorithm, which first estimates reference planes and
follows by projecting noisy points to estimated reference planes. Since the
estimated reference planes are inevitably noisy, multi-projection is applied to
stabilize the denoising performance. NPD algorithm uses a neural network to
estimate reference planes for points in noisy point clouds. With more accurate
estimations of reference planes, we are able to achieve better denoising
performances with only one-time projection. To the best of our knowledge, NPD
is the first work to denoise 3D point clouds with deep learning techniques. To
conduct the experiments, we sample 40000 point clouds from the 3D data in
ShapeNet to train a network and sample 350 point clouds from the 3D data in
ModelNet10 to test. Experimental results show that our algorithm can estimate
normal vectors of points in noisy point clouds. Comparing to five competitive
methods, the proposed algorithm achieves better denoising performance and
produces much smaller variances
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