4,819 research outputs found
Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood
We consider the problem of discriminative factor analysis for data that are
in general non-Gaussian. A Bayesian model based on the ranks of the data is
proposed. We first introduce a new {\em max-margin} version of the
rank-likelihood. A discriminative factor model is then developed, integrating
the max-margin rank-likelihood and (linear) Bayesian support vector machines,
which are also built on the max-margin principle. The discriminative factor
model is further extended to the {\em nonlinear} case through mixtures of local
linear classifiers, via Dirichlet processes. Fully local conjugacy of the model
yields efficient inference with both Markov Chain Monte Carlo and variational
Bayes approaches. Extensive experiments on benchmark and real data demonstrate
superior performance of the proposed model and its potential for applications
in computational biology.Comment: 14 pages, 7 figures, ICML 201
Unconstrained Face Verification using Deep CNN Features
In this paper, we present an algorithm for unconstrained face verification
based on deep convolutional features and evaluate it on the newly released
IARPA Janus Benchmark A (IJB-A) dataset. The IJB-A dataset includes real-world
unconstrained faces from 500 subjects with full pose and illumination
variations which are much harder than the traditional Labeled Face in the Wild
(LFW) and Youtube Face (YTF) datasets. The deep convolutional neural network
(DCNN) is trained using the CASIA-WebFace dataset. Extensive experiments on the
IJB-A dataset are provided
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