11,316 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
Multiclass latent locally linear support vector machines
Kernelized Support Vector Machines (SVM) have gained the status of off-the-shelf classifiers, able to deliver state of the art performance on almost any problem. Still, their practical use is constrained by their computational and memory complexity, which grows super-linearly with the number of training samples. In order to retain the low training and testing complexity of linear classifiers and the exibility of non linear ones, a growing, promising alternative is represented by methods that learn non-linear classifiers through local combinations of linear ones. In this paper we propose a new multi class local classifier, based on a latent SVM formulation. The proposed classifier makes use of a set of linear models that are linearly combined using sample and class specific weights. Thanks to the latent formulation, the combination coefficients are modeled as latent variables. We allow soft combinations and we provide a closed-form solution for their estimation, resulting in an efficient prediction rule. This novel formulation allows to learn in a principled way the sample specific weights and the linear classifiers, in a unique optimization problem, using a CCCP optimization procedure. Extensive experiments on ten standard UCI machine learning datasets, one large binary dataset, three character and digit recognition databases, and a visual place categorization dataset show the power of the proposed approach
Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization
Global covariance pooling in convolutional neural networks has achieved
impressive improvement over the classical first-order pooling. Recent works
have shown matrix square root normalization plays a central role in achieving
state-of-the-art performance. However, existing methods depend heavily on
eigendecomposition (EIG) or singular value decomposition (SVD), suffering from
inefficient training due to limited support of EIG and SVD on GPU. Towards
addressing this problem, we propose an iterative matrix square root
normalization method for fast end-to-end training of global covariance pooling
networks. At the core of our method is a meta-layer designed with loop-embedded
directed graph structure. The meta-layer consists of three consecutive
nonlinear structured layers, which perform pre-normalization, coupled matrix
iteration and post-compensation, respectively. Our method is much faster than
EIG or SVD based ones, since it involves only matrix multiplications, suitable
for parallel implementation on GPU. Moreover, the proposed network with ResNet
architecture can converge in much less epochs, further accelerating network
training. On large-scale ImageNet, we achieve competitive performance superior
to existing counterparts. By finetuning our models pre-trained on ImageNet, we
establish state-of-the-art results on three challenging fine-grained
benchmarks. The source code and network models will be available at
http://www.peihuali.org/iSQRT-COVComment: Accepted to CVPR 201
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