788 research outputs found
Transfer Adaptation Learning: A Decade Survey
The world we see is ever-changing and it always changes with people, things,
and the environment. Domain is referred to as the state of the world at a
certain moment. A research problem is characterized as transfer adaptation
learning (TAL) when it needs knowledge correspondence between different
moments/domains. Conventional machine learning aims to find a model with the
minimum expected risk on test data by minimizing the regularized empirical risk
on the training data, which, however, supposes that the training and test data
share similar joint probability distribution. TAL aims to build models that can
perform tasks of target domain by learning knowledge from a semantic related
but distribution different source domain. It is an energetic research filed of
increasing influence and importance, which is presenting a blowout publication
trend. This paper surveys the advances of TAL methodologies in the past decade,
and the technical challenges and essential problems of TAL have been observed
and discussed with deep insights and new perspectives. Broader solutions of
transfer adaptation learning being created by researchers are identified, i.e.,
instance re-weighting adaptation, feature adaptation, classifier adaptation,
deep network adaptation and adversarial adaptation, which are beyond the early
semi-supervised and unsupervised split. The survey helps researchers rapidly
but comprehensively understand and identify the research foundation, research
status, theoretical limitations, future challenges and under-studied issues
(universality, interpretability, and credibility) to be broken in the field
toward universal representation and safe applications in open-world scenarios.Comment: 26 pages, 4 figure
Domain Adaptive Attention Model for Unsupervised Cross-Domain Person Re-Identification
Person re-identification (Re-ID) across multiple datasets is a challenging
yet important task due to the possibly large distinctions between different
datasets and the lack of training samples in practical applications. This work
proposes a novel unsupervised domain adaption framework which transfers
discriminative representations from the labeled source domain (dataset) to the
unlabeled target domain (dataset). We propose to formulate the domain adaption
task as an one-class classification problem with a novel domain similarity
loss. Given the feature map of any image from a backbone network, a novel
domain adaptive attention model (DAAM) first automatically learns to separate
the feature map of an image to a domain-shared feature (DSH) map and a
domain-specific feature (DSP) map simultaneously. Specially, the residual
attention mechanism is designed to model DSP feature map for avoiding negative
transfer. Then, a DSH branch and a DSP branch are introduced to learn DSH and
DSP feature maps respectively. To reduce domain divergence caused by that the
source and target datasets are collected from different environments, we force
to project the DSH feature maps from different domains to a new nominal domain,
and a novel domain similarity loss is proposed based on one-class
classification. In addition, a novel unsupervised person Re-ID loss is proposed
to take full use of unlabeled target data. Extensive experiments on the
Market-1501 and DukeMTMC-reID benchmarks demonstrate state-of-the-art
performance of the proposed method. Code will be released to facilitate further
studies on the cross-domain person re-identification task
Correlation Alignment by Riemannian Metric for Domain Adaptation
Domain adaptation techniques address the problem of reducing the sensitivity
of machine learning methods to the so-called domain shift, namely the
difference between source (training) and target (test) data distributions. In
particular, unsupervised domain adaptation assumes no labels are available in
the target domain. To this end, aligning second order statistics (covariances)
of target and source domains have proven to be an effective approach ti fill
the gap between the domains. However, covariance matrices do not form a
subspace of the Euclidean space, but live in a Riemannian manifold with
non-positive curvature, making the usual Euclidean metric suboptimal to measure
distances. In this paper, we extend the idea of training a neural network with
a constraint on the covariances of the hidden layer features, by rigorously
accounting for the curved structure of the manifold of symmetric positive
definite matrices. The resulting loss function exploits a theoretically sound
geodesic distance on such manifold. Results show indeed the suboptimal nature
of the Euclidean distance. This makes us able to perform better than previous
approaches on the standard Office dataset, a benchmark for domain adaptation
techniques
Virtual Mixup Training for Unsupervised Domain Adaptation
We study the problem of unsupervised domain adaptation which aims to adapt
models trained on a labeled source domain to a completely unlabeled target
domain. Recently, the cluster assumption has been applied to unsupervised
domain adaptation and achieved strong performance. One critical factor in
successful training of the cluster assumption is to impose the
locally-Lipschitz constraint to the model. Existing methods only impose the
locally-Lipschitz constraint around the training points while miss the other
areas, such as the points in-between training data. In this paper, we address
this issue by encouraging the model to behave linearly in-between training
points. We propose a new regularization method called Virtual Mixup Training
(VMT), which is able to incorporate the locally-Lipschitz constraint to the
areas in-between training data. Unlike the traditional mixup model, our method
constructs the combination samples without using the label information,
allowing it to apply to unsupervised domain adaptation. The proposed method is
generic and can be combined with most existing models such as the recent
state-of-the-art model called VADA. Extensive experiments demonstrate that VMT
significantly improves the performance of VADA on six domain adaptation
benchmark datasets. For the challenging task of adapting MNIST to SVHN, VMT can
improve the accuracy of VADA by over 30\%. Code is available at
\url{https://github.com/xudonmao/VMT}
Domain Adaptation Meets Disentangled Representation Learning and Style Transfer
Many methods have been proposed to solve the domain adaptation problem
recently. However, the success of them implicitly funds on the assumption that
the information of domains are fully transferrable. If the assumption is not
satisfied, the effect of negative transfer may degrade domain adaptation. In
this paper, a better learning network has been proposed by considering three
tasks - domain adaptation, disentangled representation, and style transfer
simultaneously. Firstly, the learned features are disentangled into common
parts and specific parts. The common parts represent the transferrable
features, which are suitable for domain adaptation with less negative transfer.
Conversely, the specific parts characterize the unique style of each individual
domain. Based on this, the new concept of feature exchange across domains,
which can not only enhance the transferability of common features but also be
useful for image style transfer, is introduced. These designs allow us to
introduce five types of training objectives to realize the three challenging
tasks at the same time. The experimental results show that our architecture can
be adaptive well to full transfer learning and partial transfer learning upon a
well-learned disentangled representation. Besides, the trained network also
demonstrates high potential to generate style-transferred images.Comment: 22 pages, 7 figures, ACCV2018 submissio
Progressive Feature Alignment for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich
source domain to a fully-unlabeled target domain. To tackle this task, recent
approaches resort to discriminative domain transfer in virtue of pseudo-labels
to enforce the class-level distribution alignment across the source and target
domains. These methods, however, are vulnerable to the error accumulation and
thus incapable of preserving cross-domain category consistency, as the
pseudo-labeling accuracy is not guaranteed explicitly. In this paper, we
propose the Progressive Feature Alignment Network (PFAN) to align the
discriminative features across domains progressively and effectively, via
exploiting the intra-class variation in the target domain. To be specific, we
first develop an Easy-to-Hard Transfer Strategy (EHTS) and an Adaptive
Prototype Alignment (APA) step to train our model iteratively and
alternatively. Moreover, upon observing that a good domain adaptation usually
requires a non-saturated source classifier, we consider a simple yet efficient
way to retard the convergence speed of the source classification loss by
further involving a temperature variate into the soft-max function. The
extensive experimental results reveal that the proposed PFAN exceeds the
state-of-the-art performance on three UDA datasets.Comment: Accepted by CVPR 201
Asymmetric Tri-training for Unsupervised Domain Adaptation
Deep-layered models trained on a large number of labeled samples boost the
accuracy of many tasks. It is important to apply such models to different
domains because collecting many labeled samples in various domains is
expensive. In unsupervised domain adaptation, one needs to train a classifier
that works well on a target domain when provided with labeled source samples
and unlabeled target samples. Although many methods aim to match the
distributions of source and target samples, simply matching the distribution
cannot ensure accuracy on the target domain. To learn discriminative
representations for the target domain, we assume that artificially labeling
target samples can result in a good representation. Tri-training leverages
three classifiers equally to give pseudo-labels to unlabeled samples, but the
method does not assume labeling samples generated from a different domain.In
this paper, we propose an asymmetric tri-training method for unsupervised
domain adaptation, where we assign pseudo-labels to unlabeled samples and train
neural networks as if they are true labels. In our work, we use three networks
asymmetrically. By asymmetric, we mean that two networks are used to label
unlabeled target samples and one network is trained by the samples to obtain
target-discriminative representations. We evaluate our method on digit
recognition and sentiment analysis datasets. Our proposed method achieves
state-of-the-art performance on the benchmark digit recognition datasets of
domain adaptation.Comment: TBA on ICML201
A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation
Recently, DNN model compression based on network architecture design, e.g.,
SqueezeNet, attracted a lot attention. No accuracy drop on image classification
is observed on these extremely compact networks, compared to well-known models.
An emerging question, however, is whether these model compression techniques
hurt DNN's learning ability other than classifying images on a single dataset.
Our preliminary experiment shows that these compression methods could degrade
domain adaptation (DA) ability, though the classification performance is
preserved. Therefore, we propose a new compact network architecture and
unsupervised DA method in this paper. The DNN is built on a new basic module
Conv-M which provides more diverse feature extractors without significantly
increasing parameters. The unified framework of our DA method will
simultaneously learn invariance across domains, reduce divergence of feature
representations, and adapt label prediction. Our DNN has 4.1M parameters, which
is only 6.7% of AlexNet or 59% of GoogLeNet. Experiments show that our DNN
obtains GoogLeNet-level accuracy both on classification and DA, and our DA
method slightly outperforms previous competitive ones. Put all together, our DA
strategy based on our DNN achieves state-of-the-art on sixteen of total
eighteen DA tasks on popular Office-31 and Office-Caltech datasets.Comment: 2017 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR'17
-SNE: Domain Adaptation using Stochastic Neighborhood Embedding
Deep neural networks often require copious amount of labeled-data to train
their scads of parameters. Training larger and deeper networks is hard without
appropriate regularization, particularly while using a small dataset.
Laterally, collecting well-annotated data is expensive, time-consuming and
often infeasible. A popular way to regularize these networks is to simply train
the network with more data from an alternate representative dataset. This can
lead to adverse effects if the statistics of the representative dataset are
dissimilar to our target. This predicament is due to the problem of domain
shift. Data from a shifted domain might not produce bespoke features when a
feature extractor from the representative domain is used. In this paper, we
propose a new technique (-SNE) of domain adaptation that cleverly uses
stochastic neighborhood embedding techniques and a novel modified-Hausdorff
distance. The proposed technique is learnable end-to-end and is therefore,
ideally suited to train neural networks. Extensive experiments demonstrate that
-SNE outperforms the current states-of-the-art and is robust to the
variances in different datasets, even in the one-shot and semi-supervised
learning settings. -SNE also demonstrates the ability to generalize to
multiple domains concurrently.Comment: Accepted as Oral at CVPR 201
Learning Condensed and Aligned Features for Unsupervised Domain Adaptation Using Label Propagation
Unsupervised domain adaptation aiming to learn a specific task for one domain
using another domain data has emerged to address the labeling issue in
supervised learning, especially because it is difficult to obtain massive
amounts of labeled data in practice. The existing methods have succeeded by
reducing the difference between the embedded features of both domains, but the
performance is still unsatisfactory compared to the supervised learning scheme.
This is attributable to the embedded features that lay around each other but do
not align perfectly and establish clearly separable clusters. We propose a
novel domain adaptation method based on label propagation and cycle consistency
to let the clusters of the features from the two domains overlap exactly and
become clear for high accuracy. Specifically, we introduce cycle consistency to
enforce the relationship between each cluster and exploit label propagation to
achieve the association between the data from the perspective of the manifold
structure instead of a one-to-one relation. Hence, we successfully formed
aligned and discriminative clusters. We present the empirical results of our
method for various domain adaptation scenarios and visualize the embedded
features to prove that our method is critical for better domain adaptation
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