207 research outputs found
Deep Hashing Network for Unsupervised Domain Adaptation
In recent years, deep neural networks have emerged as a dominant machine
learning tool for a wide variety of application domains. However, training a
deep neural network requires a large amount of labeled data, which is an
expensive process in terms of time, labor and human expertise. Domain
adaptation or transfer learning algorithms address this challenge by leveraging
labeled data in a different, but related source domain, to develop a model for
the target domain. Further, the explosive growth of digital data has posed a
fundamental challenge concerning its storage and retrieval. Due to its storage
and retrieval efficiency, recent years have witnessed a wide application of
hashing in a variety of computer vision applications. In this paper, we first
introduce a new dataset, Office-Home, to evaluate domain adaptation algorithms.
The dataset contains images of a variety of everyday objects from multiple
domains. We then propose a novel deep learning framework that can exploit
labeled source data and unlabeled target data to learn informative hash codes,
to accurately classify unseen target data. To the best of our knowledge, this
is the first research effort to exploit the feature learning capabilities of
deep neural networks to learn representative hash codes to address the domain
adaptation problem. Our extensive empirical studies on multiple transfer tasks
corroborate the usefulness of the framework in learning efficient hash codes
which outperform existing competitive baselines for unsupervised domain
adaptation.Comment: CVPR 201
Adversarial Branch Architecture Search for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA) is a key issue in visual recognition, as
it allows to bridge different visual domains enabling robust performances in
the real world. To date, all proposed approaches rely on human expertise to
manually adapt a given UDA method (e.g. DANN) to a specific backbone
architecture (e.g. ResNet). This dependency on handcrafted designs limits the
applicability of a given approach in time, as old methods need to be constantly
adapted to novel backbones.
Existing Neural Architecture Search (NAS) approaches cannot be directly
applied to mitigate this issue, as they rely on labels that are not available
in the UDA setting. Furthermore, most NAS methods search for full
architectures, which precludes the use of pre-trained models, essential in a
vast range of UDA settings for reaching SOTA results. To the best of our
knowledge, no prior work has addressed these aspects in the context of NAS for
UDA. Here we tackle both aspects with an Adversarial Branch Architecture Search
for UDA (ABAS): i. we address the lack of target labels by a novel data-driven
ensemble approach for model selection; and ii. we search for an auxiliary
adversarial branch, attached to a pre-trained backbone, which drives the domain
alignment.
We extensively validate ABAS to improve two modern UDA techniques, DANN and
ALDA, on three standard visual recognition datasets (Office31, Office-Home and
PACS). In all cases, ABAS robustly finds the adversarial branch architectures
and parameters which yield best performances.Comment: Accepted at WACV 202
Unsupervised Domain Adaptation with Similarity Learning
The objective of unsupervised domain adaptation is to leverage features from
a labeled source domain and learn a classifier for an unlabeled target domain,
with a similar but different data distribution. Most deep learning approaches
to domain adaptation consist of two steps: (i) learn features that preserve a
low risk on labeled samples (source domain) and (ii) make the features from
both domains to be as indistinguishable as possible, so that a classifier
trained on the source can also be applied on the target domain. In general, the
classifiers in step (i) consist of fully-connected layers applied directly on
the indistinguishable features learned in (ii). In this paper, we propose a
different way to do the classification, using similarity learning. The proposed
method learns a pairwise similarity function in which classification can be
performed by computing similarity between prototype representations of each
category. The domain-invariant features and the categorical prototype
representations are learned jointly and in an end-to-end fashion. At inference
time, images from the target domain are compared to the prototypes and the
label associated with the one that best matches the image is outputed. The
approach is simple, scalable and effective. We show that our model achieves
state-of-the-art performance in different unsupervised domain adaptation
scenarios
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