14,044 research outputs found
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning
and related fields. This review asks the question: how can a classifier learn
from a source domain and generalize to a target domain? We present a
categorization of approaches, divided into, what we refer to as, sample-based,
feature-based and inference-based methods. Sample-based methods focus on
weighting individual observations during training based on their importance to
the target domain. Feature-based methods revolve around on mapping, projecting
and representing features such that a source classifier performs well on the
target domain and inference-based methods incorporate adaptation into the
parameter estimation procedure, for instance through constraints on the
optimization procedure. Additionally, we review a number of conditions that
allow for formulating bounds on the cross-domain generalization error. Our
categorization highlights recurring ideas and raises questions important to
further research.Comment: 20 pages, 5 figure
Multi-View Face Recognition From Single RGBD Models of the Faces
This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks
Non-sparse Linear Representations for Visual Tracking with Online Reservoir Metric Learning
Most sparse linear representation-based trackers need to solve a
computationally expensive L1-regularized optimization problem. To address this
problem, we propose a visual tracker based on non-sparse linear
representations, which admit an efficient closed-form solution without
sacrificing accuracy. Moreover, in order to capture the correlation information
between different feature dimensions, we learn a Mahalanobis distance metric in
an online fashion and incorporate the learned metric into the optimization
problem for obtaining the linear representation. We show that online metric
learning using proximity comparison significantly improves the robustness of
the tracking, especially on those sequences exhibiting drastic appearance
changes. Furthermore, in order to prevent the unbounded growth in the number of
training samples for the metric learning, we design a time-weighted reservoir
sampling method to maintain and update limited-sized foreground and background
sample buffers for balancing sample diversity and adaptability. Experimental
results on challenging videos demonstrate the effectiveness and robustness of
the proposed tracker.Comment: Appearing in IEEE Conf. Computer Vision and Pattern Recognition, 201
Investigating the Effects of Word Substitution Errors on Sentence Embeddings
A key initial step in several natural language processing (NLP) tasks
involves embedding phrases of text to vectors of real numbers that preserve
semantic meaning. To that end, several methods have been recently proposed with
impressive results on semantic similarity tasks. However, all of these
approaches assume that perfect transcripts are available when generating the
embeddings. While this is a reasonable assumption for analysis of written text,
it is limiting for analysis of transcribed text. In this paper we investigate
the effects of word substitution errors, such as those coming from automatic
speech recognition errors (ASR), on several state-of-the-art sentence embedding
methods. To do this, we propose a new simulator that allows the experimenter to
induce ASR-plausible word substitution errors in a corpus at a desired word
error rate. We use this simulator to evaluate the robustness of several
sentence embedding methods. Our results show that pre-trained neural sentence
encoders are both robust to ASR errors and perform well on textual similarity
tasks after errors are introduced. Meanwhile, unweighted averages of word
vectors perform well with perfect transcriptions, but their performance
degrades rapidly on textual similarity tasks for text with word substitution
errors.Comment: 4 Pages, 2 figures. Copyright IEEE 2019. Accepted and to appear in
the Proceedings of the 44th International Conference on Acoustics, Speech,
and Signal Processing 2019 (IEEE-ICASSP-2019), May 12-17 in Brighton, U.K.
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