96 research outputs found
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
Federated Domain Generalization: A Survey
Machine learning typically relies on the assumption that training and testing
distributions are identical and that data is centrally stored for training and
testing. However, in real-world scenarios, distributions may differ
significantly and data is often distributed across different devices,
organizations, or edge nodes. Consequently, it is imperative to develop models
that can effectively generalize to unseen distributions where data is
distributed across different domains. In response to this challenge, there has
been a surge of interest in federated domain generalization (FDG) in recent
years. FDG combines the strengths of federated learning (FL) and domain
generalization (DG) techniques to enable multiple source domains to
collaboratively learn a model capable of directly generalizing to unseen
domains while preserving data privacy. However, generalizing the federated
model under domain shifts is a technically challenging problem that has
received scant attention in the research area so far. This paper presents the
first survey of recent advances in this area. Initially, we discuss the
development process from traditional machine learning to domain adaptation and
domain generalization, leading to FDG as well as provide the corresponding
formal definition. Then, we categorize recent methodologies into four classes:
federated domain alignment, data manipulation, learning strategies, and
aggregation optimization, and present suitable algorithms in detail for each
category. Next, we introduce commonly used datasets, applications, evaluations,
and benchmarks. Finally, we conclude this survey by providing some potential
research topics for the future
Non-Iterative Scribble-Supervised Learning with Pacing Pseudo-Masks for Medical Image Segmentation
Scribble-supervised medical image segmentation tackles the limitation of
sparse masks. Conventional approaches alternate between: labeling pseudo-masks
and optimizing network parameters. However, such iterative two-stage paradigm
is unwieldy and could be trapped in poor local optima since the networks
undesirably regress to the erroneous pseudo-masks. To address these issues, we
propose a non-iterative method where a stream of varying (pacing) pseudo-masks
teach a network via consistency training, named PacingPseudo. Our motivation
lies first in a non-iterative process. Interestingly, it can be achieved
gracefully by a siamese architecture, wherein a stream of pseudo-masks
naturally assimilate a stream of predicted masks during training. Second, we
make the consistency training effective with two necessary designs: (i) entropy
regularization to obtain high-confidence pseudo-masks for effective teaching;
and (ii) distorted augmentations to create discrepancy between the pseudo-mask
and predicted-mask streams for consistency regularization. Third, we devise a
new memory bank mechanism that provides an extra source of ensemble features to
complement scarce labeled pixels. The efficacy of the proposed PacingPseudo is
validated on three public medical image datasets, including the segmentation
tasks of abdominal multi-organs, cardiac structures, and myocardium. Extensive
experiments demonstrate our PacingPseudo improves the baseline by large margins
and consistently outcompetes several previous methods. In some cases, our
PacingPseudo achieves comparable performance with its fully-supervised
counterparts, showing the feasibility of our method for the challenging
scribble-supervised segmentation applications. The code and scribble
annotations will be publicly available.Comment: 12 pages, 8 figure
Neural Unsupervised Domain Adaptation in NLP—A Survey
Deep neural networks excel at learning from labeled data and achieve
state-of-the-art results on a wide array of Natural Language Processing tasks.
In contrast, learning from unlabeled data, especially under domain shift,
remains a challenge. Motivated by the latest advances, in this survey we review
neural unsupervised domain adaptation techniques which do not require labeled
target domain data. This is a more challenging yet a more widely applicable
setup. We outline methods, from early approaches in traditional non-neural
methods to pre-trained model transfer. We also revisit the notion of domain,
and we uncover a bias in the type of Natural Language Processing tasks which
received most attention. Lastly, we outline future directions, particularly the
broader need for out-of-distribution generalization of future intelligent NLP
Similarity-Based Framework for Unsupervised Domain Adaptation: Peer Reviewing Policy for Pseudo-Labeling
The inherent dependency of deep learning models on labeled data is a well-known problem and one of the barriers that slows down the integration of such methods into different fields of applied sciences and engineering, in which experimental and numerical methods can easily generate a colossal amount of unlabeled data. This paper proposes an unsupervised domain adaptation methodology that mimics the peer review process to label new observations in a different domain from the training set. The approach evaluates the validity of a hypothesis using domain knowledge acquired from the training set through a similarity analysis, exploring the projected feature space to examine the class centroid shifts. The methodology is tested on a binary classification problem, where synthetic images of cubes and cylinders in different orientations are generated. The methodology improves the accuracy of the object classifier from 60% to around 90% in the case of a domain shift in physical feature space without human labeling
A few-shot graph Laplacian-based approach for improving the accuracy of low-fidelity data
Low-fidelity data is typically inexpensive to generate but inaccurate. On the
other hand, high-fidelity data is accurate but expensive to obtain.
Multi-fidelity methods use a small set of high-fidelity data to enhance the
accuracy of a large set of low-fidelity data. In the approach described in this
paper, this is accomplished by constructing a graph Laplacian using the
low-fidelity data and computing its low-lying spectrum. This spectrum is then
used to cluster the data and identify points that are closest to the centroids
of the clusters. High-fidelity data is then acquired for these key points.
Thereafter, a transformation that maps every low-fidelity data point to its
bi-fidelity counterpart is determined by minimizing the discrepancy between the
bi- and high-fidelity data at the key points, and to preserve the underlying
structure of the low-fidelity data distribution. The latter objective is
achieved by relying, once again, on the spectral properties of the graph
Laplacian. This method is applied to a problem in solid mechanics and another
in aerodynamics. In both cases, this methods uses a small fraction of
high-fidelity data to significantly improve the accuracy of a large set of
low-fidelity data
Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery
Deep-learning frameworks have made remarkable progress thanks to the creation of large annotated datasets such as ImageNet, which has over one million training images. Although this works well for color (RGB) imagery, labeled datasets for other sensor modalities (e.g., multispectral and hyperspectral) are minuscule in comparison. This is because annotated datasets are expensive and man-power intensive to complete; and since this would be impractical to accomplish for each type of sensor, current state-of-the-art approaches in computer vision are not ideal for remote sensing problems. The shortage of annotated remote sensing imagery beyond the visual spectrum has forced researchers to embrace unsupervised feature extracting frameworks. These features are learned on a per-image basis, so they tend to not generalize well across other datasets. In this dissertation, we propose three new strategies for learning feature extracting frameworks with only a small quantity of annotated image data; including 1) self-taught feature learning, 2) domain adaptation with synthetic imagery, and 3) semi-supervised classification. ``Self-taught\u27\u27 feature learning frameworks are trained with large quantities of unlabeled imagery, and then these networks extract spatial-spectral features from annotated data for supervised classification. Synthetic remote sensing imagery can be used to boot-strap a deep convolutional neural network, and then we can fine-tune the network with real imagery. Semi-supervised classifiers prevent overfitting by jointly optimizing the supervised classification task along side one or more unsupervised learning tasks (i.e., reconstruction). Although obtaining large quantities of annotated image data would be ideal, our work shows that we can make due with less cost-prohibitive methods which are more practical to the end-user
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