476 research outputs found
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
Domain Adaptive Neural Networks for Object Recognition
We propose a simple neural network model to deal with the domain adaptation
problem in object recognition. Our model incorporates the Maximum Mean
Discrepancy (MMD) measure as a regularization in the supervised learning to
reduce the distribution mismatch between the source and target domains in the
latent space. From experiments, we demonstrate that the MMD regularization is
an effective tool to provide good domain adaptation models on both SURF
features and raw image pixels of a particular image data set. We also show that
our proposed model, preceded by the denoising auto-encoder pretraining,
achieves better performance than recent benchmark models on the same data sets.
This work represents the first study of MMD measure in the context of neural
networks
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
The I4U consortium was established to facilitate a joint entry to NIST
speaker recognition evaluations (SRE). The latest edition of such joint
submission was in SRE 2018, in which the I4U submission was among the
best-performing systems. SRE'18 also marks the 10-year anniversary of I4U
consortium into NIST SRE series of evaluation. The primary objective of the
current paper is to summarize the results and lessons learned based on the
twelve sub-systems and their fusion submitted to SRE'18. It is also our
intention to present a shared view on the advancements, progresses, and major
paradigm shifts that we have witnessed as an SRE participant in the past decade
from SRE'08 to SRE'18. In this regard, we have seen, among others, a paradigm
shift from supervector representation to deep speaker embedding, and a switch
of research challenge from channel compensation to domain adaptation.Comment: 5 page
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
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions
Generative Adversarial Networks (GANs) is a novel class of deep generative
models which has recently gained significant attention. GANs learns complex and
high-dimensional distributions implicitly over images, audio, and data.
However, there exists major challenges in training of GANs, i.e., mode
collapse, non-convergence and instability, due to inappropriate design of
network architecture, use of objective function and selection of optimization
algorithm. Recently, to address these challenges, several solutions for better
design and optimization of GANs have been investigated based on techniques of
re-engineered network architectures, new objective functions and alternative
optimization algorithms. To the best of our knowledge, there is no existing
survey that has particularly focused on broad and systematic developments of
these solutions. In this study, we perform a comprehensive survey of the
advancements in GANs design and optimization solutions proposed to handle GANs
challenges. We first identify key research issues within each design and
optimization technique and then propose a new taxonomy to structure solutions
by key research issues. In accordance with the taxonomy, we provide a detailed
discussion on different GANs variants proposed within each solution and their
relationships. Finally, based on the insights gained, we present the promising
research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table
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