An Active Learning Framework for Alpha Matting

Good trimap is essential for high-quality alpha matte. However, making high-quality trimap is hardwork, especially for complex images. In this paper, an active learning framework is proposed to make high quality trimap. There are two active learning methods which are employed: minimization of uncertainty sampling (MUS) and maximization of expected model output change (EMOC). MUS model finds the informative area in image which can decrease the uncertain sampling of alpha matte. EMOC model finds the important areas in image which can give the maximum expected output change of alpha matte. Two methods are combined to define the active map. Active map shows important areas which are informative in image. It can help users to make high quality trimap. The analysis and evaluation of benchmark datasets show that proposed method is effective.

Download Full-text

Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme

Entropy ◽

10.3390/e21100988 ◽

2019 ◽

Vol 21 (10) ◽

pp. 988 ◽

Cited By ~ 4

Author(s):

Fazakis ◽

Kanas ◽

Aridas ◽

Karlos ◽

Kotsiantis

Keyword(s):

Active Learning ◽

Supervised Learning ◽

Unlabeled Data ◽

Classification Algorithms ◽

Training Phase ◽

Learning Methods ◽

Training Scheme ◽

Wide Range ◽

Benchmark Datasets ◽

Scientific Fields

One of the major aspects affecting the performance of the classification algorithms is the amount of labeled data which is available during the training phase. It is widely accepted that the labeling procedure of vast amounts of data is both expensive and time-consuming since it requires the employment of human expertise. For a wide variety of scientific fields, unlabeled examples are easy to collect but hard to handle in a useful manner, thus improving the contained information for a subject dataset. In this context, a variety of learning methods have been studied in the literature aiming to efficiently utilize the vast amounts of unlabeled data during the learning process. The most common approaches tackle problems of this kind by individually applying active learning or semi-supervised learning methods. In this work, a combination of active learning and semi-supervised learning methods is proposed, under a common self-training scheme, in order to efficiently utilize the available unlabeled data. The effective and robust metrics of the entropy and the distribution of probabilities of the unlabeled set, to select the most sufficient unlabeled examples for the augmentation of the initial labeled set, are used. The superiority of the proposed scheme is validated by comparing it against the base approaches of supervised, semi-supervised, and active learning in the wide range of fifty-five benchmark datasets.

Download Full-text

Active Learning Methods for Low Resource End-to-End Speech Recognition

10.21437/interspeech.2019-2316 ◽

2019 ◽

Cited By ~ 2

Author(s):

Karan Malhotra ◽

Shubham Bansal ◽

Sriram Ganapathy

Keyword(s):

Speech Recognition ◽

Active Learning ◽

Learning Methods ◽

Low Resource ◽

End To End

Download Full-text

Active Learning, Cooperative Active Learning, and Passive Learning Methods in an Accounting Information Systems Course

Issues in Accounting Education ◽

10.2308/iace-51366 ◽

2015 ◽

Vol 32 (2) ◽

pp. 1-16 ◽

Cited By ~ 21

Author(s):

Jennifer Riley ◽

Kerry Ward

Keyword(s):

Information Systems ◽

Active Learning ◽

Student Performance ◽

Accounting Information ◽

Data Availability ◽

Exam Performance ◽

Learning Methods ◽

Accounting Information Systems ◽

Passive Learning ◽

Learning Conditions

ABSTRACT We report the results of a study to examine the effectiveness of active versus passive learning methods in the accounting information systems area. Two groups of students completed an assignment under two active learning conditions (individual and cooperative), while a third group covered the same topic in a passive lecture. Our findings indicate support for active learning, measured through student performance on exam questions and student feedback on a questionnaire. However, compared to passive learners, we find significantly improved exam performance only for students who work individually in an active environment. Students in the cooperative active environment posted exam scores that were not statistically different from passive participants' scores. Students in both individual and cooperative active environments reported positive feedback on satisfaction, perceived learning, and effectiveness of the method. We conclude that active learning enhances student outcomes, particularly for those who work individually. Data Availability: Data are available upon request.

Download Full-text

Active Learning Methods Applied in the Course “Digital Electronics” on the Topic “Arithmetic Circuits Using FPGA Design”

2019 29th Annual Conference of the European Association for Education in Electrical and Information Engineering (EAEEIE) ◽

10.1109/eaeeie46886.2019.9000421 ◽

2019 ◽

Cited By ~ 2

Author(s):

Adriana Borodzhieva ◽

Iordan Stoev ◽

Valentin Mutkov

Keyword(s):

Active Learning ◽

Arithmetic Circuits ◽

Fpga Design ◽

Learning Methods ◽

Digital Electronics

Download Full-text

Drift Compensation on Massive Online Electronic-Nose Responses

Chemosensors ◽

10.3390/chemosensors9040078 ◽

2021 ◽

Vol 9 (4) ◽

pp. 78

Author(s):

Jianhua Cao ◽

Tao Liu ◽

Jianjun Chen ◽

Tao Yang ◽

Xiuxiu Zhu ◽

...

Keyword(s):

Active Learning ◽

Gas Sensor ◽

Electronic Nose ◽

Effective Means ◽

Learning Paradigm ◽

Class Label ◽

Learning Framework ◽

Sensor Drift ◽

Drift Compensation ◽

Noisy Labels

Gas sensor drift is an important issue of electronic nose (E-nose) systems. This study follows this concern under the condition that requires an instant drift compensation with massive online E-nose responses. Recently, an active learning paradigm has been introduced to such condition. However, it does not consider the “noisy label” problem caused by the unreliability of its labeling process in real applications. Thus, we have proposed a class-label appraisal methodology and associated active learning framework to assess and correct the noisy labels. To evaluate the performance of the proposed methodologies, we used the datasets from two E-nose systems. The experimental results show that the proposed methodology helps the E-noses achieve higher accuracy with lower computation than the reference methods do. Finally, we can conclude that the proposed class-label appraisal mechanism is an effective means of enhancing the robustness of active learning-based E-nose drift compensation.

Download Full-text

An All-Batch Loss for Constructing Prediction Intervals

Applied Sciences ◽

10.3390/app11041728 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1728

Author(s):

Hua Zhong ◽

Li Xu

Keyword(s):

Gradient Descent ◽

State Of The Art ◽

Prediction Interval ◽

Feedforward Neural Networks ◽

Important Research ◽

Likelihood Principle ◽

High Quality ◽

Construction Methods ◽

Important Research Topic ◽

Benchmark Datasets

The prediction interval (PI) is an important research topic in reliability analyses and decision support systems. Data size and computation costs are two of the issues which may hamper the construction of PIs. This paper proposes an all-batch (AB) loss function for constructing high quality PIs. Taking the full advantage of the likelihood principle, the proposed loss makes it possible to train PI generation models using the gradient descent (GD) method for both small and large batches of samples. With the structure of dual feedforward neural networks (FNNs), a high-quality PI generation framework is introduced, which can be adapted to a variety of problems including regression analysis. Numerical experiments were conducted on the benchmark datasets; the results show that higher-quality PIs were achieved using the proposed scheme. Its reliability and stability were also verified in comparison with various state-of-the-art PI construction methods.

Download Full-text

Text Mining of Hazard and Operability Analysis Reports Based on Active Learning

Processes ◽

10.3390/pr9071178 ◽

2021 ◽

Vol 9 (7) ◽

pp. 1178

Author(s):

Zhenhua Wang ◽

Beike Zhang ◽

Dong Gao

Keyword(s):

Active Learning ◽

Named Entity Recognition ◽

Entity Recognition ◽

Chemical System ◽

Chemical Safety ◽

High Quality ◽

Data Set ◽

Final Model ◽

Named Entity ◽

Sampling Algorithms

In the field of chemical safety, a named entity recognition (NER) model based on deep learning can mine valuable information from hazard and operability analysis (HAZOP) text, which can guide experts to carry out a new round of HAZOP analysis, help practitioners optimize the hidden dangers in the system, and be of great significance to improve the safety of the whole chemical system. However, due to the standardization and professionalism of chemical safety analysis text, it is difficult to improve the performance of traditional models. To solve this problem, in this study, an improved method based on active learning is proposed, and three novel sampling algorithms are designed, Variation of Token Entropy (VTE), HAZOP Confusion Entropy (HCE) and Amplification of Least Confidence (ALC), which improve the ability of the model to understand HAZOP text. In this method, a part of data is used to establish the initial model. The sampling algorithm is then used to select high-quality samples from the data set. Finally, these high-quality samples are used to retrain the whole model to obtain the final model. The experimental results show that the performance of the VTE, HCE, and ALC algorithms are better than that of random sampling algorithms. In addition, compared with other methods, the performance of the traditional model is improved effectively by the method proposed in this paper, which proves that the method is reliable and advanced.

Download Full-text