142,838 research outputs found
A Survey on Deep Semi-supervised Learning
Deep semi-supervised learning is a fast-growing field with a range of
practical applications. This paper provides a comprehensive survey on both
fundamentals and recent advances in deep semi-supervised learning methods from
model design perspectives and unsupervised loss functions. We first present a
taxonomy for deep semi-supervised learning that categorizes existing methods,
including deep generative methods, consistency regularization methods,
graph-based methods, pseudo-labeling methods, and hybrid methods. Then we offer
a detailed comparison of these methods in terms of the type of losses,
contributions, and architecture differences. In addition to the past few years'
progress, we further discuss some shortcomings of existing methods and provide
some tentative heuristic solutions for solving these open problems.Comment: 24 pages, 6 figure
A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams
Unlabelled data appear in many domains and are particularly relevant to
streaming applications, where even though data is abundant, labelled data is
rare. To address the learning problems associated with such data, one can
ignore the unlabelled data and focus only on the labelled data (supervised
learning); use the labelled data and attempt to leverage the unlabelled data
(semi-supervised learning); or assume some labels will be available on request
(active learning). The first approach is the simplest, yet the amount of
labelled data available will limit the predictive performance. The second
relies on finding and exploiting the underlying characteristics of the data
distribution. The third depends on an external agent to provide the required
labels in a timely fashion. This survey pays special attention to methods that
leverage unlabelled data in a semi-supervised setting. We also discuss the
delayed labelling issue, which impacts both fully supervised and
semi-supervised methods. We propose a unified problem setting, discuss the
learning guarantees and existing methods, explain the differences between
related problem settings. Finally, we review the current benchmarking practices
and propose adaptations to enhance them
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation
Medical image segmentation is a fundamental and critical step in many
image-guided clinical approaches. Recent success of deep learning-based
segmentation methods usually relies on a large amount of labeled data, which is
particularly difficult and costly to obtain especially in the medical imaging
domain where only experts can provide reliable and accurate annotations.
Semi-supervised learning has emerged as an appealing strategy and been widely
applied to medical image segmentation tasks to train deep models with limited
annotations. In this paper, we present a comprehensive review of recently
proposed semi-supervised learning methods for medical image segmentation and
summarized both the technical novelties and empirical results. Furthermore, we
analyze and discuss the limitations and several unsolved problems of existing
approaches. We hope this review could inspire the research community to explore
solutions for this challenge and further promote the developments in medical
image segmentation field
CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification
Recently, seismic facies classification based on convolutional neural
networks (CNN) has garnered significant research interest. However, existing
CNN-based supervised learning approaches necessitate massive labeled data.
Labeling is laborious and time-consuming, particularly for 3D seismic data
volumes. To overcome this challenge, we propose a semi-supervised method based
on pixel-level contrastive learning, termed CONSS, which can efficiently
identify seismic facies using only 1% of the original annotations. Furthermore,
the absence of a unified data division and standardized metrics hinders the
fair comparison of various facies classification approaches. To this end, we
develop an objective benchmark for the evaluation of semi-supervised methods,
including self-training, consistency regularization, and the proposed CONSS.
Our benchmark is publicly available to enable researchers to objectively
compare different approaches. Experimental results demonstrate that our
approach achieves state-of-the-art performance on the F3 survey
Label-efficient Time Series Representation Learning: A Review
The scarcity of labeled data is one of the main challenges of applying deep
learning models on time series data in the real world. Therefore, several
approaches, e.g., transfer learning, self-supervised learning, and
semi-supervised learning, have been recently developed to promote the learning
capability of deep learning models from the limited time series labels. In this
survey, for the first time, we provide a novel taxonomy to categorize existing
approaches that address the scarcity of labeled data problem in time series
data based on their dependency on external data sources. Moreover, we present a
review of the recent advances in each approach and conclude the limitations of
the current works and provide future directions that could yield better
progress in the field.Comment: Under Revie
Optimizing machine learning methods to discover strong gravitational lenses in the Deep Lens Survey
Machine learning models can greatly improve the search for strong
gravitational lenses in imaging surveys by reducing the amount of human
inspection required. In this work, we test the performance of supervised,
semi-supervised, and unsupervised learning algorithms trained with the ResNetV2
neural network architecture on their ability to efficiently find strong
gravitational lenses in the Deep Lens Survey (DLS). We use galaxy images from
the survey, combined with simulated lensed sources, as labeled data in our
training datasets. We find that models using semi-supervised learning along
with data augmentations (transformations applied to an image during training,
e.g., rotation) and Generative Adversarial Network (GAN) generated images yield
the best performance. They offer 5--10 times better precision across all recall
values compared to supervised algorithms. Applying the best performing models
to the full 20 deg DLS survey, we find 3 Grade-A lens candidates within the
top 17 image predictions from the model. This increases to 9 Grade-A and 13
Grade-B candidates when % ( images) of the model predictions are
visually inspected. This is the sky density of lens
candidates compared to current shallower wide-area surveys (such as the Dark
Energy Survey), indicating a trove of lenses awaiting discovery in upcoming
deeper all-sky surveys. These results suggest that pipelines tasked with
finding strong lens systems can be highly efficient, minimizing human effort.
We additionally report spectroscopic confirmation of the lensing nature of two
Grade-A candidates identified by our model, further validating our methods.Comment: 23 pages, 15 figures (including appendix), published in MNRA
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