62 research outputs found
Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs
State-of-the-art image-set matching techniques typically implicitly model
each image-set with a Gaussian distribution. Here, we propose to go beyond
these representations and model image-sets as probability distribution
functions (PDFs) using kernel density estimators. To compare and match
image-sets, we exploit Csiszar f-divergences, which bear strong connections to
the geodesic distance defined on the space of PDFs, i.e., the statistical
manifold. Furthermore, we introduce valid positive definite kernels on the
statistical manifolds, which let us make use of more powerful classification
schemes to match image-sets. Finally, we introduce a supervised dimensionality
reduction technique that learns a latent space where f-divergences reflect the
class labels of the data. Our experiments on diverse problems, such as
video-based face recognition and dynamic texture classification, evidence the
benefits of our approach over the state-of-the-art image-set matching methods
From Review to Rating: Exploring Dependency Measures for Text Classification
Various text analysis techniques exist, which attempt to uncover unstructured
information from text. In this work, we explore using statistical dependence
measures for textual classification, representing text as word vectors. Student
satisfaction scores on a 3-point scale and their free text comments written
about university subjects are used as the dataset. We have compared two textual
representations: a frequency word representation and term frequency
relationship to word vectors, and found that word vectors provide a greater
accuracy. However, these word vectors have a large number of features which
aggravates the burden of computational complexity. Thus, we explored using a
non-linear dependency measure for feature selection by maximizing the
dependence between the text reviews and corresponding scores. Our quantitative
and qualitative analysis on a student satisfaction dataset shows that our
approach achieves comparable accuracy to the full feature vector, while being
an order of magnitude faster in testing. These text analysis and feature
reduction techniques can be used for other textual data applications such as
sentiment analysis.Comment: 8 page
Multi-component Image Translation for Deep Domain Generalization
Domain adaption (DA) and domain generalization (DG) are two closely related
methods which are both concerned with the task of assigning labels to an
unlabeled data set. The only dissimilarity between these approaches is that DA
can access the target data during the training phase, while the target data is
totally unseen during the training phase in DG. The task of DG is challenging
as we have no earlier knowledge of the target samples. If DA methods are
applied directly to DG by a simple exclusion of the target data from training,
poor performance will result for a given task. In this paper, we tackle the
domain generalization challenge in two ways. In our first approach, we propose
a novel deep domain generalization architecture utilizing synthetic data
generated by a Generative Adversarial Network (GAN). The discrepancy between
the generated images and synthetic images is minimized using existing domain
discrepancy metrics such as maximum mean discrepancy or correlation alignment.
In our second approach, we introduce a protocol for applying DA methods to a DG
scenario by excluding the target data from the training phase, splitting the
source data to training and validation parts, and treating the validation data
as target data for DA. We conduct extensive experiments on four cross-domain
benchmark datasets. Experimental results signify our proposed model outperforms
the current state-of-the-art methods for DG.Comment: Accepted in WACV 201
Distribution-Matching Embedding for Visual Domain Adaptation
Domain-invariant representations are key to addressing the domain shift problem where the
training and test examples follow different distributions. Existing techniques that have attempted
to match the distributions of the source and target domains typically compare these distributions in
the original feature space. This space, however, may not be directly suitable for such a comparison,
since some of the features may have been distorted by the domain shift, or may be domain specific.
In this paper, we introduce a Distribution-Matching Embedding approach: An unsupervised
domain adaptation method that overcomes this issue by mapping the data to a latent space where
the distance between the empirical distributions of the source and target examples is minimized. In
other words, we seek to extract the information that is invariant across the source and target data.
In particular, we study two different distances to compare the source and target distributions: the
Maximum Mean Discrepancy and the Hellinger distance. Furthermore, we show that our approach
allows us to learn either a linear embedding, or a nonlinear one. We demonstrate the benefits of our
approach on the tasks of visual object recognition, text categorization, and WiFi localization
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