Lost in Transduction: Transductive Transfer Learning in Text Classification

Moreo, Alejandro; Esuli, Andrea; Sebastiani, Fabrizio

doi:10.1145/3453146

Cited by 14 publications

(5 citation statements)

References 69 publications

(109 reference statements)

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“…China's patent investigation started late, but with the rapid development of the times and the rapid progress of science and technology, the importance of patent investigation has become increasingly prominent. Moreo et al deeply studied the content level of patent text and extracted the actual content of patent text, including title, abstract, and background summary [14]; Esuli et al put forward an automatic patent classification method based on statistical distribution and analyzed patents. According to the characteristics of text data, two metrics were added to the patent text: the interclass dispersion weight factor and the position weight factor.…”

Section: Research On Classification Of Patent Informationmentioning

confidence: 99%

[Retracted] Application of Punitive Damages in Intellectual Property Law in Complex Network Environment

2022

Journal of Environmental and Public Health

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Many IP (intellectual property) will also be infringed, and it will become more and more serious, causing huge economic losses to people or groups who own IP. If punitive damages can be made for intentional but intentional infringement of IP, it will help to stop intentional infringement of IP, and it can also give due compensation to the obligee’s loss. Based on this, this paper analyzes the introduction and implementation of punitive damages in IP law. In this paper, a relatively efficient text classification method is realized by using the method of deep learning and model design. Similar texts in the training set are grouped into a group, and each group is considered as a common text vector. Then, a threshold is set to deal with clusters higher than the threshold, and the training set is reformed. Through the experimental results, the feature extraction effect of this model is improved, the classification accuracy is constantly improved, and the final convergence rate of this model reaches 95%. It has been verified that the system can realize the automatic classification of basic Chinese patent texts.

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Section: Research On Classification Of Patent Informationmentioning

confidence: 99%

[Retracted] Application of Punitive Damages in Intellectual Property Law in Complex Network Environment

2022

Journal of Environmental and Public Health

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“…It is used to describe the prediction process from domain to specific in statistical learning theory [95]. It learns concrete instances and not universal rules as in induction [96].…”

Section: Transductive Learningmentioning

confidence: 99%

“…A new inference definition is given when the model estimates a functional value [97]. The inference principle emerges when the best results are derived from limited knowledge [95,98]. The k-nearest neighbor algorithm is used in transductive algorithm for prediction, but not modeling of training data [99,100].…”

Section: Transductive Learningmentioning

confidence: 99%

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

et al. 2023

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Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL.

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“…35,36 Genetic programming and distributional correspondence indexing-based transductive transfer learning is used for document and text classification. 37,38 The crop classification is essential for agriculture and food management, but sometimes there is only a small amount of ground truth data. To solve this issue, authors used a transductive transfer learning-based approach 10 for crop classification.…”

Section: Transductive Transfer Learningmentioning

confidence: 99%

Deep transductive transfer learning for automatic target recognition

Sami

Nasrabadi

Rao

2023

Automatic Target Recognition XXXIII

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One of the major obstacles in designing an automatic target recognition (ATR) algorithm, is that there are often labeled images in one domain (i.e., infrared source domain) but no annotated images in the other target domains (i.e., visible, SAR, LIDAR). Therefore, automatically annotating these images is essential to build a robust classifier in the target domain based on the labeled images of the source domain. Transductive transfer learning is an effective way to adapt a network to a new target domain by utilizing a pretrained ATR network in the source domain. We propose an unpaired transductive transfer learning framework where a CycleGAN model and a well-trained ATR classifier in the source domain are used to construct an ATR classifier in the target domain without having any labeled data in the target domain. We employ a CycleGAN model to transfer the mid-wave infrared (MWIR) images to visible (VIS) domain images (or visible to MWIR domain). To train the transductive CycleGAN, we optimize a cost function consisting of the adversarial, identity, cycle-consistency, and categorical cross-entropy loss for both the source and target classifiers. In this paper, we perform a detailed experimental analysis on the challenging DSIAC ATR dataset. The dataset consists of ten classes of vehicles at different poses and distances ranging from 1-5 kilometers on both the MWIR and VIS domains. In our experiment, we assume that the images in the VIS domain are the unlabeled target dataset. We first detect and crop the vehicles from the raw images and then project them into a common distance of 2 kilometers. Our proposed transductive CycleGAN achieves 71.56% accuracy in classifying the visible domain vehicles in the DSIAC ATR dataset.

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Lost in Transduction: Transductive Transfer Learning in Text Classification

Cited by 14 publications

References 69 publications

[Retracted] Application of Punitive Damages in Intellectual Property Law in Complex Network Environment

[Retracted] Application of Punitive Damages in Intellectual Property Law in Complex Network Environment

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

Deep transductive transfer learning for automatic target recognition

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