scholarly journals Learning Meta Model for Zero- and Few-Shot Face Anti-Spoofing

2020 ◽  
Vol 34 (07) ◽  
pp. 11916-11923 ◽  
Author(s):  
Yunxiao Qin ◽  
Chenxu Zhao ◽  
Xiangyu Zhu ◽  
Zezheng Wang ◽  
Zitong Yu ◽  
...  
Keyword(s):  
Face Recognition ◽  
Supervised Learning ◽  
Large Scale ◽  
Training Data ◽  
Learning Problem ◽  
Meta Model ◽  
Meta Learning ◽  
Recognition Systems

Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various predefined presentation attacks, which need large scale training data to cover as many attacks as possible. However, the trained model is easy to overfit several common attacks and is still vulnerable to unseen attacks. To overcome this challenge, the detector should: 1) learn discriminative features that can generalize to unseen spoofing types from predefined presentation attacks; 2) quickly adapt to new spoofing types by learning from both the predefined attacks and a few examples of the new spoofing types. Therefore, we define face anti-spoofing as a zero- and few-shot learning problem. In this paper, we propose a novel Adaptive Inner-update Meta Face Anti-Spoofing (AIM-FAS) method to tackle this problem through meta-learning. Specifically, AIM-FAS trains a meta-learner focusing on the task of detecting unseen spoofing types by learning from predefined living and spoofing faces and a few examples of new attacks. To assess the proposed approach, we propose several benchmarks for zero- and few-shot FAS. Experiments show its superior performances on the presented benchmarks to existing methods in existing zero-shot FAS protocols.

MetaTP

2021 ◽  
Vol 5 (3) ◽  
pp. 1-28
Author(s):  
Weida Zhong ◽  
Qiuling Suo ◽  
Abhishek Gupta ◽  
Xiaowei Jia ◽  
Chunming Qiao ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Multivariate Time Series ◽  
Modern Society ◽  
Training Data ◽  
Traffic Prediction ◽  
Temporal Prediction ◽  
Reference Space ◽  
Meta Learning ◽  
Real World Datasets

With the popularity of smartphones, large-scale road sensing data is being collected to perform traffic prediction, which is an important task in modern society. Due to the nature of the roving sensors on smartphones, the collected traffic data which is in the form of multivariate time series, is often temporally sparse and unevenly distributed across regions. Moreover, different regions can have different traffic patterns, which makes it challenging to adapt models learned from regions with sufficient training data to target regions. Given that many regions may have very sparse data, it is also impossible to build individual models for each region separately. In this paper, we propose a meta-learning based framework named MetaTP to overcome these challenges. MetaTP has two key parts, i.e., basic traffic prediction network (base model) and meta-knowledge transfer. In base model, a two-layer interpolation network is employed to map original time series onto uniformly-spaced reference time points, so that temporal prediction can be effectively performed in the reference space. The meta-learning framework is employed to transfer knowledge from source regions with a large amount of data to target regions with a few data examples via fast adaptation, in order to improve model generalizability on target regions. Moreover, we use two memory networks to capture the global patterns of spatial and temporal information across regions. We evaluate the proposed framework on two real-world datasets, and experimental results show the effectiveness of the proposed framework.

scholarly journals Multi-Task Self-Supervised Learning for Disfluency Detection

2020 ◽  
Vol 34 (05) ◽  
pp. 9193-9200
Author(s):  
Shaolei Wang ◽  
Wangxiang Che ◽  
Qi Liu ◽  
Pengda Qin ◽  
Ting Liu ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Large Scale ◽  
Experimental Results ◽  
Training Data ◽  
Competitive Performance ◽  
Test Set ◽  
Full Dataset ◽  
Sentence Classification ◽  
Trained Network

Most existing approaches to disfluency detection heavily rely on human-annotated data, which is expensive to obtain in practice. To tackle the training data bottleneck, we investigate methods for combining multiple self-supervised tasks-i.e., supervised tasks where data can be collected without manual labeling. First, we construct large-scale pseudo training data by randomly adding or deleting words from unlabeled news data, and propose two self-supervised pre-training tasks: (i) tagging task to detect the added noisy words. (ii) sentence classification to distinguish original sentences from grammatically-incorrect sentences. We then combine these two tasks to jointly train a network. The pre-trained network is then fine-tuned using human-annotated disfluency detection training data. Experimental results on the commonly used English Switchboard test set show that our approach can achieve competitive performance compared to the previous systems (trained using the full dataset) by using less than 1% (1000 sentences) of the training data. Our method trained on the full dataset significantly outperforms previous methods, reducing the error by 21% on English Switchboard.

Combining Self-supervised Learning and Active Learning for Disfluency Detection

2022 ◽  
Vol 21 (3) ◽  
pp. 1-25
Author(s):  
Shaolei Wang ◽  
Zhongyuan Wang ◽  
Wanxiang Che ◽  
Sendong Zhao ◽  
Ting Liu
Keyword(s):  
Neural Network ◽  
Active Learning ◽  
Supervised Learning ◽  
Large Scale ◽  
Training Data ◽  
Fine Tuning ◽  
Training Dataset ◽  
Performance Gap ◽  
Annotation Costs ◽  
Trained Neural Network

Spoken language is fundamentally different from the written language in that it contains frequent disfluencies or parts of an utterance that are corrected by the speaker. Disfluency detection (removing these disfluencies) is desirable to clean the input for use in downstream NLP tasks. Most existing approaches to disfluency detection heavily rely on human-annotated data, which is scarce and expensive to obtain in practice. To tackle the training data bottleneck, in this work, we investigate methods for combining self-supervised learning and active learning for disfluency detection. First, we construct large-scale pseudo training data by randomly adding or deleting words from unlabeled data and propose two self-supervised pre-training tasks: (i) a tagging task to detect the added noisy words and (ii) sentence classification to distinguish original sentences from grammatically incorrect sentences. We then combine these two tasks to jointly pre-train a neural network. The pre-trained neural network is then fine-tuned using human-annotated disfluency detection training data. The self-supervised learning method can capture task-special knowledge for disfluency detection and achieve better performance when fine-tuning on a small annotated dataset compared to other supervised methods. However, limited in that the pseudo training data are generated based on simple heuristics and cannot fully cover all the disfluency patterns, there is still a performance gap compared to the supervised models trained on the full training dataset. We further explore how to bridge the performance gap by integrating active learning during the fine-tuning process. Active learning strives to reduce annotation costs by choosing the most critical examples to label and can address the weakness of self-supervised learning with a small annotated dataset. We show that by combining self-supervised learning with active learning, our model is able to match state-of-the-art performance with just about 10% of the original training data on both the commonly used English Switchboard test set and a set of in-house annotated Chinese data.

Data set entity recognition based on distant supervision

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pengcheng Li ◽  
Qikai Liu ◽  
Qikai Cheng ◽  
Wei Lu
Keyword(s):  
Supervised Learning ◽  
Large Scale ◽  
Data Augmentation ◽  
Scientific Literature ◽  
Neural Model ◽  
Training Data ◽  
Entity Recognition ◽  
Data Set ◽  
Content Type ◽  
Augmentation Techniques

Purpose This paper aims to identify data set entities in scientific literature. To address poor recognition caused by a lack of training corpora in existing studies, a distant supervised learning-based approach is proposed to identify data set entities automatically from large-scale scientific literature in an open domain. Design/methodology/approach Firstly, the authors use a dictionary combined with a bootstrapping strategy to create a labelled corpus to apply supervised learning. Secondly, a bidirectional encoder representation from transformers (BERT)-based neural model was applied to identify data set entities in the scientific literature automatically. Finally, two data augmentation techniques, entity replacement and entity masking, were introduced to enhance the model generalisability and improve the recognition of data set entities. Findings In the absence of training data, the proposed method can effectively identify data set entities in large-scale scientific papers. The BERT-based vectorised representation and data augmentation techniques enable significant improvements in the generality and robustness of named entity recognition models, especially in long-tailed data set entity recognition. Originality/value This paper provides a practical research method for automatically recognising data set entities in scientific literature. To the best of the authors’ knowledge, this is the first attempt to apply distant learning to the study of data set entity recognition. The authors introduce a robust vectorised representation and two data augmentation strategies (entity replacement and entity masking) to address the problem inherent in distant supervised learning methods, which the existing research has mostly ignored. The experimental results demonstrate that our approach effectively improves the recognition of data set entities, especially long-tailed data set entities.

scholarly journals Cross domain meta-network for sketch face recognition

2021 ◽  
Vol 336 ◽  
pp. 06007
Author(s):  
Yuying Shao ◽  
Lin Cao ◽  
Changwu Chen ◽  
Kangning Du
Keyword(s):  
Face Recognition ◽  
Small Sample ◽  
Training Data ◽  
Data Sets ◽  
Training Strategy ◽  
Cross Domain ◽  
Meta Learning ◽  
Average Loss ◽  
Optical Domain ◽  
Small Sample Problem

Because of the large modal difference between sketch image and optical image, and the problem that traditional deep learning methods are easy to overfit in the case of a small amount of training data, the Cross Domain Meta-Network for sketch face recognition method is proposed. This method first designs a meta-learning training strategy to solve the small sample problem, and then proposes entropy average loss and cross domain adaptive loss to reduce the modal difference between the sketch domain and the optical domain. The experimental results on UoM-SGFS and PRIP-VSGC sketch face data sets show that this method and other sketch face recognition methods.

scholarly journals Discriminative Aging Subspace Learning for Age Estimation

2021 ◽  
Author(s):  
Manisha Sawant ◽  
Kishor Bhurchandi
Keyword(s):  
Factor Analysis ◽  
Face Recognition ◽  
Age Estimation ◽  
Large Scale ◽  
Subspace Learning ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Age Related ◽  
Age Factor ◽  
Recognition Systems

Abstract Hidden factor analysis ( HFA ) has been widely used in age invariant face recognition systems. It decomposes facial features into independent age factor and identity factor. Age invariant face recognition systems utilize identity factor for face recognition; however, the age factor remains unutilized . The age component of the hidden factor analysis model depends on the subject's age, hence it carries a significant age related information. In this paper, we propose the HFA model based discriminative manifold learning method for age estimation. Further, multiple regression methods are applied on low dimensional features learned from the aging subspace. Extensive experiments are performed on a large scale aging database MORPH II and the accuracy of our method is found superior to the current state-of-the-art methods.

Recognition of Large-Scale ncRNA Data Using a Novel Multitask Cross-Learning 0-Order TSK Fuzzy Classifier

2020 ◽  
Vol 10 (2) ◽  
pp. 502-507 ◽  
Author(s):  
Yizhang Jiang ◽  
Jiaqi Zhu ◽  
Xiaoqing Gu ◽  
Jing Xue ◽  
Kaifa Zhao ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Fuzzy Systems ◽  
Large Scale ◽  
Tumor Formation ◽  
Training Data ◽  
Small Scale ◽  
Learning Problem ◽  
Fuzzy Classifier ◽  
Learning Mechanism ◽  
Takagi Sugeno

Recognizing noncoding ribonucleic acid (ncRNA) data is helpful in realizing the regulation of tumor formation and certain aspects of life mechanisms, such as growth, differentiation, development, and immunity. However, the scale of ncRNA data is usually very large. Using machine learning (ML) methods to automatically analyze these data can obtain more precise results than manually analyzing these data, but the traditional ML algorithms can process only small-scale training data. To solve this problem, a novel multitask cross-learning 0-order Takagi–Sugeno–Kang fuzzy classifier (MT-CL-0-TSK-FC) is proposed that uses a multitask cross-learning mechanism to solve the large-scale learning problem of ncRNA data. In addition, the proposed MT-CL-0-TSK-FC method naturally inherits the interpretability of traditional fuzzy systems and eventually generates an interpretable rulesbased database to recognize the ncRNA data. The experimental results indicate that the proposed MT-CL-0TSK-FC method has a faster running time and better classification accuracy than traditional ML methods.

scholarly journals Hierarchical Classification of Urban ALS Data by Using Geometry and Intensity Information

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4583 ◽  
Author(s):  
Xiaoqiang Liu ◽  
Yanming Chen ◽  
Shuyi Li ◽  
Liang Cheng ◽  
Manchun Li
Keyword(s):  
Supervised Learning ◽  
Laser Scanning ◽  
Large Scale ◽  
Three Dimensional ◽  
Hierarchical Classification ◽  
Training Data ◽  
Classification Model ◽  
Learning Method ◽  
Intensity Information

Airborne laser scanning (ALS) can acquire both geometry and intensity information of geo-objects, which is important in mapping a large-scale three-dimensional (3D) urban environment. However, the intensity information recorded by ALS will be changed due to the flight height and atmospheric attenuation, which decreases the robustness of the trained supervised classifier. This paper proposes a hierarchical classification method by separately using geometry and intensity information of urban ALS data. The method uses supervised learning for stable geometry information and unsupervised learning for fluctuating intensity information. The experiment results show that the proposed method can utilize the intensity information effectively, based on three aspects, as below. (1) The proposed method improves the accuracy of classification result by using intensity. (2) When the ALS data to be classified are acquired under the same conditions as the training data, the performance of the proposed method is as good as the supervised learning method. (3) When the ALS data to be classified are acquired under different conditions from the training data, the performance of the proposed method is better than the supervised learning method. Therefore, the classification model derived from the proposed method can be transferred to other ALS data whose intensity is inconsistent with the training data. Furthermore, the proposed method can contribute to the hierarchical use of some other ALS information, such as multi-spectral information.

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