700 research outputs found
Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images
Semantic segmentation models based on convolutional neural networks (CNNs)
have gained much attention in relation to remote sensing and have achieved
remarkable performance for the extraction of buildings from high-resolution
aerial images. However, the issue of limited generalization for unseen images
remains. When there is a domain gap between the training and test datasets,
CNN-based segmentation models trained by a training dataset fail to segment
buildings for the test dataset. In this paper, we propose segmentation networks
based on a domain adaptive transfer attack (DATA) scheme for building
extraction from aerial images. The proposed system combines the domain transfer
and adversarial attack concepts. Based on the DATA scheme, the distribution of
the input images can be shifted to that of the target images while turning
images into adversarial examples against a target network. Defending
adversarial examples adapted to the target domain can overcome the performance
degradation due to the domain gap and increase the robustness of the
segmentation model. Cross-dataset experiments and the ablation study are
conducted for the three different datasets: the Inria aerial image labeling
dataset, the Massachusetts building dataset, and the WHU East Asia dataset.
Compared to the performance of the segmentation network without the DATA
scheme, the proposed method shows improvements in the overall IoU. Moreover, it
is verified that the proposed method outperforms even when compared to feature
adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 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
Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes
Symptom information is primarily documented in free-text clinical notes and
is not directly accessible for downstream applications. To address this
challenge, information extraction approaches that can handle clinical language
variation across different institutions and specialties are needed. In this
paper, we present domain generalization for symptom extraction using
pretraining and fine-tuning data that differs from the target domain in terms
of institution and/or specialty and patient population. We extract symptom
events using a transformer-based joint entity and relation extraction method.
To reduce reliance on domain-specific features, we propose a domain
generalization method that dynamically masks frequent symptoms words in the
source domain. Additionally, we pretrain the transformer language model (LM) on
task-related unlabeled texts for better representation. Our experiments
indicate that masking and adaptive pretraining methods can significantly
improve performance when the source domain is more distant from the target
domain
Kernelized Hashcode Representations for Relation Extraction
Kernel methods have produced state-of-the-art results for a number of NLP
tasks such as relation extraction, but suffer from poor scalability due to the
high cost of computing kernel similarities between natural language structures.
A recently proposed technique, kernelized locality-sensitive hashing (KLSH),
can significantly reduce the computational cost, but is only applicable to
classifiers operating on kNN graphs. Here we propose to use random subspaces of
KLSH codes for efficiently constructing an explicit representation of NLP
structures suitable for general classification methods. Further, we propose an
approach for optimizing the KLSH model for classification problems by
maximizing an approximation of mutual information between the KLSH codes
(feature vectors) and the class labels. We evaluate the proposed approach on
biomedical relation extraction datasets, and observe significant and robust
improvements in accuracy w.r.t. state-of-the-art classifiers, along with
drastic (orders-of-magnitude) speedup compared to conventional kernel methods.Comment: To appear in the proceedings of conference, AAAI-1
Data efficient deep learning for medical image analysis: A survey
The rapid evolution of deep learning has significantly advanced the field of
medical image analysis. However, despite these achievements, the further
enhancement of deep learning models for medical image analysis faces a
significant challenge due to the scarcity of large, well-annotated datasets. To
address this issue, recent years have witnessed a growing emphasis on the
development of data-efficient deep learning methods. This paper conducts a
thorough review of data-efficient deep learning methods for medical image
analysis. To this end, we categorize these methods based on the level of
supervision they rely on, encompassing categories such as no supervision,
inexact supervision, incomplete supervision, inaccurate supervision, and only
limited supervision. We further divide these categories into finer
subcategories. For example, we categorize inexact supervision into multiple
instance learning and learning with weak annotations. Similarly, we categorize
incomplete supervision into semi-supervised learning, active learning, and
domain-adaptive learning and so on. Furthermore, we systematically summarize
commonly used datasets for data efficient deep learning in medical image
analysis and investigate future research directions to conclude this survey.Comment: Under Revie
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