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Corrections to "Learning to Learn Adaptive Classifier-Predictor for Few-Shot Learning"

IEEE transactions on neural networks and learning systems 2021年第8期32卷 3784页

作者： Nan Lai Meina Kan Chunrui Han Xingguang Song Shiguang Shan Key Laboratory of Intelligent Information Processing Institute of Computing Technology (ICT) Chinese Academy of Sciences (CAS) Beijing China University of Chinese Academy of Sciences Beijing China Huawei Technologies Company Ltd. Shenzhen China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China

In the above article [1], the results of "Fully-supervised (Upper bound)" in Tables III and IV were inadvertently set to intermediate records that were used as placeholders. This error has no effect on any of the interpretations and conclusions. Tables I and II of this amendment show the corrected results (highlighted in italics) of the original Tables III and IV.

关键词： Predictive models Feature extraction Deep learning Learning systems Adaptive systems

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Direct speech-to-image translation

arXiv

引用

arXiv 2020年

作者： Li, Jiguo Zhang, Xinfeng Jia, Chuanmin Xu, Jizheng Zhang, Li Wang, Yue Ma, Siwei Gao, Wen Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China National Engineering Laboratory for Video Technology School of Electronic Engineering and Computer Science Peking University Beijing100871 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Institute of Digital Media School of Electronic Engineering and Computer Science Peking University Beijing100871 China Peng Cheng Lab Shenzhen China Bytedance Inc

Direct speech-to-image translation without text is an interesting and useful topic due to the potential applications in human-computer interaction, art creation, computer-aided design. etc. Not to mention that many languages have no writing form. However, as far as we know, it has not been well-studied how to translate the speech signals into images directly and how well they can be translated. In this paper, we attempt to translate the speech signals into the image signals without the transcription stage. Specifically, a speech encoder is designed to represent the input speech signals as an embedding feature, and it is trained with a pretrained image encoder using teacher-student learning to obtain better generalization ability on new classes. Subsequently, a stacked generative adversarial network is used to synthesize high-quality images conditioned on the embedding feature. Experimental results on both synthesized and real data show that our proposed method is effective to translate the raw speech signals into images without the middle text representation. Ablation study gives more insights about our method. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Relative influence maximization in competitive social networks

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science China(information sciences) 2017年第10期60卷 252-254页

作者： Dingda YANG Xiangwen LIAO Huawei SHEN Xueqi CHENG Guolong CHEN College of Physics and Information Engineering Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University Institute of Computing Technology Chinese Academy of Sciences

In many realistic scenarios,such as political election and viral marketing,two opposite opinions,i.e.,positive opinion and negative opinion,spread simultaneously in the same social networks[1,2].Consequently,to achiev... 详细信息

关键词： net RIM Relative influence maximization in competitive social networks Figure

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Transferable Contrastive Network for Generalized Zero-Shot Learning

Transferable Contrastive Network for Generalized Zero-Shot L...

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International Conference on Computer Vision (ICCV)

作者： Huajie Jiang Ruiping Wang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Shanghai Institute of Microsystem and Information Technology CAS Shanghai China School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Zero-shot learning (ZSL) is a challenging problem that aims to recognize the target categories without seen data, where semantic information is leveraged to transfer knowledge from some source classes. Although ZSL has made great progress in recent years, most existing approaches are easy to overfit the sources classes in generalized zero-shot learning (GZSL) task, which indicates that they learn little knowledge about target classes. To tackle such problem, we propose a novel Transferable Contrastive Network (TCN) that explicitly transfers knowledge from the source classes to the target classes. It automatically contrasts one image with different classes to judge whether they are consistent or not. By exploiting the class similarities to make knowledge transfer from source images to similar target classes, our approach is more robust to recognize the target images. Experiments on five benchmark datasets show the superiority of our approach for GZSL.

关键词： Semantics Visualization Image recognition Robustness Target recognition Task analysis Fuses

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AttentionDTA: prediction of drug–target binding affinity using attention model

AttentionDTA: prediction of drug–target binding affinity us...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qichang Zhao Fen Xiao Mengyun Yang Yaohang Li Jianxin Wang Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan China School of Computer Science and Engineering Central South University Changsha Hunan China Old Dominion University Norfolk Virginia USA

ISBN: (纸本)9781728118680

In bioinformatics, machine learning-based prediction of drug-target interaction (DTI) plays an important role in virtual screening of drug discovery. DTI prediction, which have been treated as a binary classification problem, depends on the concentration of two molecules, the interaction between two molecules, and other factors. The degree of affinity between a drug molecule (such as a drug compound) and a target molecule (such as a receptor or protein kinase) reflects how tightly the drug binds to a particular target and is quantified by the measurement which can reflect more detailed and specific information than binary relationship. In this study, we proposed an end-to-end model, named AttentionDTA, based on deep learning, which associates attention mechanism to predict the binding affinity of DTI. The novelty in this work is to use attentional mechanisms to consider which subsequences in a protein are more important for a drug and which subsequences in a drug are more important for a protein when predicting its affinity. So that the representational ability of the model is stronger. The model uses one-dimensional Convolution Neural Networks (1D-CNNs) to extract the abstract information of drug and protein, and makes the drug and protein representations mutually adapt through the attention mechanisms. We evaluate our model on two established drug-target affinity benchmark datasets, Davis and KIBA. The model outperforms DeepDTA, a state-of-the-art deep learning method for drug-target binding affinity prediction, with better Mean Squared Error (MSE), Concordance Index (CI), rm 2 , and Area Under Precision Recall Curve (AUPR). Our results show that the attention-based model can effectively extract effective representations by calculating the weight of the representation between the drug and the protein. Finally, we visualize the attention weight. It proves our model can obtain the information of binding sites.

关键词： bioinformatics biology computing convolutional neural nets drugs learning (artificial intelligence) mean square error methods molecular biophysics pattern classification proteins Mean square error Pharmaceutical Preparations biology computing molecular biology Pattern recognition Bioinformatics binding affinity Drug Compounding Protein affinity Diffusion Tensor Imaging Proteins

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Brain EEG time series selection: A novel graph-based approach for classification

Brain EEG time series selection: A novel graph-based approac...

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2018 SIAM International Conference on Data Mining, SDM 2018

作者： Dai, Chenglong Wu, Jia Pi, Dechang Cui, Lin Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Computing Macquarie University NSW2109 Australia Intelligent Information Processing Laboratory Suzhou University Suzhou234000 China

Brain Electroencephalography (EEG) classification is widely applied to analyze cerebral diseases in recent years. Unfortunately, invalid/noisy EEGs degrade the diagnosis performance and most previously developed methods ignore the necessity of EEG selection for classification. To this end, this paper proposes a novel maximum weight clique-based EEG selection approach, named mwcEEGs, to map EEG selection to searching maximum similarity-weighted cliques from an improved Fréchet distance-weighted undirected EEG graph simultaneously considering edge weights and vertex weights. Our mwcEEGs improves the classification performance by selecting intra-clique pairwise similar and inter-clique discriminative EEGs with similarity threshold δ. Experimental results demonstrate the algorithm effectiveness compared with the state-of the-art time series selection algorithms on real-world EEG datasets. © 2018 by SIAM.

关键词： Electroencephalography

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Temporal Knowledge Propagation for Image-to-Video Person Re-Identification

Temporal Knowledge Propagation for Image-to-Video Person Re-...

引用

International Conference on Computer Vision (ICCV)

作者： Xinqian Gu Bingpeng Ma Hong Chang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

In many scenarios of Person Re-identification (Re-ID), the gallery set consists of lots of surveillance videos and the query is just an image, thus Re-ID has to be conducted between image and videos. Compared with videos, still person images lack temporal information. Besides, the information asymmetry between image and video features increases the difficulty in matching images and videos. To solve this problem, we propose a novel Temporal Knowledge Propagation (TKP) method which propagates the temporal knowledge learned by the video representation network to the image representation network. Specifically, given the input videos, we enforce the image representation network to fit the outputs of video representation network in a shared feature space. With back propagation, temporal knowledge can be transferred to enhance the image features and the information asymmetry problem can be alleviated. With additional classification and integrated triplet losses, our model can learn expressive and discriminative image and video features for image-to-video re-identification. Extensive experiments demonstrate the effectiveness of our method and the overall results on two widely used datasets surpass the state-of-the-art methods by a large margin.

关键词： Videos Image representation Feature extraction Knowledge engineering Visualization Robustness Training

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Fully Learnable Group Convolution for Acceleration of Deep Neural Networks

Fully Learnable Group Convolution for Acceleration of Deep N...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xijun Wang Meina Kan Shiguang Shan Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China

ISBN: (纸本)9781728132945

Benefitted from its great success on many tasks, deep learning is increasingly used on low-computational-cost devices, e.g. smartphone, embedded devices, etc. To reduce the high computational and memory cost, in this work, we propose a fully learnable group convolution module (FLGC for short) which is quite efficient and can be embedded into any deep neural networks for acceleration. Specifically, our proposed method automatically learns the group structure in the training stage in a fully end-to-end manner, leading to a better structure than the existing pre-defined, two-steps, or iterative strategies. Moreover, our method can be further combined with depthwise separable convolution, resulting in 5 times acceleration than the vanilla Resnet50 on single CPU. An additional advantage is that in our FLGC the number of groups can be set as any value, but not necessarily 2^k as in most existing methods, meaning better tradeoff between accuracy and speed. As evaluated in our experiments, our method achieves better performance than existing learnable group convolution and standard group convolution when using the same number of groups.

关键词： acceleration Convolution numerical invariant Group Structure Neural network

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Magnetotelluric signal-noise separation method based on SVM–CEEMDWT

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Applied Geophysics 2019年第2期16卷 160-170,252-253页

作者： Li Jin Cai Jin Tang Jing-Tian Li Guang Zhang Xian Xu Zhi-Min Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and EngineeringHunan Normal UniversityChangsha 410081China School of Geosciences and Info-Physics Key Laboratory of Metallogenic Prediction of Non-Ferrous Metals and Geological Environment MonitorMinistry of EducationCentral South UniversityChangsha 410083China State Key Laboratory Breeding Base of Nuclear Resources and Environment East China University of TechnologyNanchang 330013China

To better retain useful weak low-frequency magnetotelluric(MT)signals with strong interference during MT data processing,we propose a SVM-CEEMDWT based MT data signal-noise separation method,which extracts the weak MT signal affected by strong ***,the approximate entropy,fuzzy entropy,sample entropy,and Lempel-Ziv(LZ)complexity are extracted from the magnetotelluric ***,four robust parameters are used as the inputs to the support vector machine(SVM)to train the sample library and build a model based on the different complexity of *** on this model,we can only consider time series with strong interference when using the complementary ensemble empirical mode decomposition(CEEMD)and wavelet threshold(WT)for noise *** results suggest that the SVM based on the robust parameters can distinguish the time periods with strong interference well before noise *** with the CEEMD WT,the proposed SVM-CEEMDWT method retains more low-frequency low-variability information,and the apparent resistivity curve is smoother and more ***,the results better reflect the deep electrical structure in the field.

关键词： SVM-CEEMDWT magnetotelluric signal-noise separation MT data processing

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Modeling semantic relationship in multi-turn conversations with hierarchical latent variables

arXiv

引用

arXiv 2019年

作者： Shen, Lei Feng, Yang Zhan, Haolan Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

Multi-turn conversations consist of complex semantic structures, and it is still a challenge to generate coherent and diverse responses given previous utterances. It's practical that a conversation takes place under a background, meanwhile, the query and response are usually most related and they are consistent in topic but also different in content. However, little work focuses on such hierarchical relationship among utterances. To address this problem, we propose a Conversational Semantic Relationship RNN (CSRR) model to construct the dependency explicitly. The model contains latent variables in three hierarchies. The discourse-level one captures the global background, the pair-level one stands for the common topic information between query and response, and the utterance-level ones try to represent differences in content. Experimental results show that our model significantly improves the quality of responses in terms of fluency, coherence and diversity compared to baseline methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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