Labeled Data Generation with Inexact Supervision
- URL: http://arxiv.org/abs/2106.04716v1
- Date: Tue, 8 Jun 2021 22:22:26 GMT
- Title: Labeled Data Generation with Inexact Supervision
- Authors: Enyan Dai, Kai Shu, Yiwei Sun, Suhang Wang
- Abstract summary: In this paper, we study a novel problem of labeled data generation with inexact supervision.
We propose a novel generative framework named as ADDES which can synthesize high-quality labeled data for target classification tasks.
- Score: 33.110134862501546
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The recent advanced deep learning techniques have shown the promising results
in various domains such as computer vision and natural language processing. The
success of deep neural networks in supervised learning heavily relies on a
large amount of labeled data. However, obtaining labeled data with target
labels is often challenging due to various reasons such as cost of labeling and
privacy issues, which challenges existing deep models. In spite of that, it is
relatively easy to obtain data with \textit{inexact supervision}, i.e., having
labels/tags related to the target task. For example, social media platforms are
overwhelmed with billions of posts and images with self-customized tags, which
are not the exact labels for target classification tasks but are usually
related to the target labels. It is promising to leverage these tags (inexact
supervision) and their relations with target classes to generate labeled data
to facilitate the downstream classification tasks. However, the work on this is
rather limited. Therefore, we study a novel problem of labeled data generation
with inexact supervision. We propose a novel generative framework named as
ADDES which can synthesize high-quality labeled data for target classification
tasks by learning from data with inexact supervision and the relations between
inexact supervision and target classes. Experimental results on image and text
datasets demonstrate the effectiveness of the proposed ADDES for generating
realistic labeled data from inexact supervision to facilitate the target
classification task.
Related papers
- A Self Supervised StyleGAN for Image Annotation and Classification with
Extremely Limited Labels [35.43549147657739]
We propose SS-StyleGAN, a self-supervised approach for image annotation and classification suitable for extremely small annotated datasets.
We show that the proposed method attains strong classification results using small labeled datasets of sizes 50 and even 10.
arXiv Detail & Related papers (2023-12-26T09:46:50Z) - Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations [91.67511167969934]
imprecise label learning (ILL) is a framework for the unification of learning with various imprecise label configurations.
We demonstrate that ILL can seamlessly adapt to partial label learning, semi-supervised learning, noisy label learning, and, more importantly, a mixture of these settings.
arXiv Detail & Related papers (2023-05-22T04:50:28Z) - AutoWS: Automated Weak Supervision Framework for Text Classification [1.748907524043535]
We propose a novel framework for increasing the efficiency of weak supervision process while decreasing the dependency on domain experts.
Our method requires a small set of labeled examples per label class and automatically creates a set of labeling functions to assign noisy labels to numerous unlabeled data.
arXiv Detail & Related papers (2023-02-07T07:12:05Z) - Losses over Labels: Weakly Supervised Learning via Direct Loss
Construction [71.11337906077483]
Programmable weak supervision is a growing paradigm within machine learning.
We propose Losses over Labels (LoL) as it creates losses directly from ofs without going through the intermediate step of a label.
We show that LoL improves upon existing weak supervision methods on several benchmark text and image classification tasks.
arXiv Detail & Related papers (2022-12-13T22:29:14Z) - Label Matching Semi-Supervised Object Detection [85.99282969977541]
Semi-supervised object detection has made significant progress with the development of mean teacher driven self-training.
Label mismatch problem is not yet fully explored in the previous works, leading to severe confirmation bias during self-training.
We propose a simple yet effective LabelMatch framework from two different yet complementary perspectives.
arXiv Detail & Related papers (2022-06-14T05:59:41Z) - Debiased Pseudo Labeling in Self-Training [77.83549261035277]
Deep neural networks achieve remarkable performances on a wide range of tasks with the aid of large-scale labeled datasets.
To mitigate the requirement for labeled data, self-training is widely used in both academia and industry by pseudo labeling on readily-available unlabeled data.
We propose Debiased, in which the generation and utilization of pseudo labels are decoupled by two independent heads.
arXiv Detail & Related papers (2022-02-15T02:14:33Z) - Data Consistency for Weakly Supervised Learning [15.365232702938677]
Training machine learning models involves using large amounts of human-annotated data.
We propose a novel weak supervision algorithm that processes noisy labels, i.e., weak signals.
We show that it significantly outperforms state-of-the-art weak supervision methods on both text and image classification tasks.
arXiv Detail & Related papers (2022-02-08T16:48:19Z) - PseudoSeg: Designing Pseudo Labels for Semantic Segmentation [78.35515004654553]
We present a re-design of pseudo-labeling to generate structured pseudo labels for training with unlabeled or weakly-labeled data.
We demonstrate the effectiveness of the proposed pseudo-labeling strategy in both low-data and high-data regimes.
arXiv Detail & Related papers (2020-10-19T17:59:30Z) - Adversarial Knowledge Transfer from Unlabeled Data [62.97253639100014]
We present a novel Adversarial Knowledge Transfer framework for transferring knowledge from internet-scale unlabeled data to improve the performance of a classifier.
An important novel aspect of our method is that the unlabeled source data can be of different classes from those of the labeled target data, and there is no need to define a separate pretext task.
arXiv Detail & Related papers (2020-08-13T08:04:27Z) - Deep Categorization with Semi-Supervised Self-Organizing Maps [0.0]
This article presents a semi-supervised model, called Batch Semi-Supervised Self-Organizing Map (Batch SS-SOM)
The results show that Batch SS-SOM is a good option for semi-supervised classification and clustering.
It performs well in terms of accuracy and clustering error, even with a small number of labeled samples.
arXiv Detail & Related papers (2020-06-17T22:00:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.