EAAE: A Generative Adversarial Mechanism Based Classfication Method for Small-scale Datasets

[1]  Timothy M. Hospedales,et al.  Meta-Learning in Neural Networks: A Survey , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Jun Yu,et al.  Hierarchical Deep Click Feature Prediction for Fine-Grained Image Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Vijay Rana,et al.  Deep learning based semantic personalized recommendation system , 2021, Int. J. Inf. Manag. Data Insights.

[4]  Jinfeng Li,et al.  Unified Cross-domain Classification via Geometric and Statistical Adaptations , 2021, Pattern Recognit..

[5]  Sung-Bae Cho,et al.  Deep CNN transferred from VAE and GAN for classifying irritating noise in automobile , 2020, Neurocomputing.

[6]  Lionel M. Ni,et al.  Generalizing from a Few Examples , 2020, ACM Comput. Surv..

[7]  Sercan O. Arik,et al.  TabNet: Attentive Interpretable Tabular Learning , 2019, AAAI.

[8]  Giorgio Visani,et al.  Metrics for Multi-Class Classification: an Overview , 2020, ArXiv.

[9]  Yan Hong,et al.  F2GAN: Fusing-and-Filling GAN for Few-shot Image Generation , 2020, ACM Multimedia.

[10]  Yan Hong,et al.  Matchinggan: Matching-Based Few-Shot Image Generation , 2020, 2020 IEEE International Conference on Multimedia and Expo (ICME).

[11]  Qingming Huang,et al.  Spatial Pyramid-Enhanced NetVLAD With Weighted Triplet Loss for Place Recognition , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Ling Shao,et al.  Zero-VAE-GAN: Generating Unseen Features for Generalized and Transductive Zero-Shot Learning , 2020, IEEE Transactions on Image Processing.

[13]  Ahmad B. A. Hassanat,et al.  Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review , 2019, Big Data.

[14]  Mohamed Atri,et al.  Traffic Signs Detection for Real-World Application of an Advanced Driving Assisting System Using Deep Learning , 2019, Neural Processing Letters.

[15]  Mohamed Atri,et al.  Traffic Signs Detection for Real-World Application of an Advanced Driving Assisting System Using Deep Learning , 2019, Neural Processing Letters.

[16]  Jinfeng Li,et al.  Domain Adaptation with Few Labeled Source Samples by Graph Regularization , 2019, Neural Processing Letters.

[17]  Diederik P. Kingma,et al.  An Introduction to Variational Autoencoders , 2019, Found. Trends Mach. Learn..

[18]  Geoffrey E. Hinton,et al.  When Does Label Smoothing Help? , 2019, NeurIPS.

[19]  Quoc V. Le,et al.  AutoAugment: Learning Augmentation Strategies From Data , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Guiguang Ding,et al.  Deep Transfer Learning for Image Emotion Analysis: Reducing Marginal and Joint Distribution Discrepancies Together , 2019, Neural Processing Letters.

[21]  James T. Kwok,et al.  Generalizing from a Few Examples , 2019, ACM Comput. Surv..

[22]  R. Janghel,et al.  Recent Deep Learning Techniques, Challenges and Its Applications for Medical Healthcare System: A Review , 2019, Neural Processing Letters.

[23]  Rogério Schmidt Feris,et al.  SpotTune: Transfer Learning Through Adaptive Fine-Tuning , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Xindong Wu,et al.  Object Detection With Deep Learning: A Review , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[25]  Quoc V. Le,et al.  Do Better ImageNet Models Transfer Better? , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  José Carlos Príncipe,et al.  Understanding Autoencoders with Information Theoretic Concepts , 2018, Neural Networks.

[27]  Jun Yu,et al.  Multimodal Face-Pose Estimation With Multitask Manifold Deep Learning , 2019, IEEE Transactions on Industrial Informatics.

[28]  Purvi Prajapati,et al.  Study and Analysis of Decision Tree Based Classification Algorithms , 2018, International Journal of Computer Sciences and Engineering.

[29]  Chao Yang,et al.  A Survey on Deep Transfer Learning , 2018, ICANN.

[30]  Seema Shah,et al.  A Review of Machine Learning and Deep Learning Applications , 2018, 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA).

[31]  Hassan Abbar,et al.  A comparative study of algorithms constructing decision trees: ID3 and C4.5 , 2018, LOPAL '18.

[32]  Tanveer F. Syeda-Mahmood,et al.  Semi-supervised learning with generative adversarial networks for chest X-ray classification with ability of data domain adaptation , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[33]  Yoshua Bengio,et al.  Fine-grained attention mechanism for neural machine translation , 2018, Neurocomputing.

[34]  Tao Xiang,et al.  Learning to Compare: Relation Network for Few-Shot Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[35]  M. Kawulok,et al.  Selecting training sets for support vector machines: a review , 2019, Artificial Intelligence Review.

[36]  V. B. Surya Prasath,et al.  Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier - A Review , 2017, Big Data.

[37]  Chen Sun,et al.  Revisiting Unreasonable Effectiveness of Data in Deep Learning Era , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[38]  Matthias Hein,et al.  Variants of RMSProp and Adagrad with Logarithmic Regret Bounds , 2017, ICML.

[39]  Richard S. Zemel,et al.  Prototypical Networks for Few-shot Learning , 2017, NIPS.

[40]  Ji Feng,et al.  Deep Forest: Towards An Alternative to Deep Neural Networks , 2017, IJCAI.

[41]  Justin Salamon,et al.  Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification , 2016, IEEE Signal Processing Letters.

[42]  Martial Hebert,et al.  Learning to Learn: Model Regression Networks for Easy Small Sample Learning , 2016, ECCV.

[43]  Ole Winther,et al.  Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.

[44]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[45]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[46]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[47]  Michael I. Jordan,et al.  Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.

[48]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[49]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[50]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[51]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[52]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[53]  Chong Wang,et al.  Stochastic variational inference , 2012, J. Mach. Learn. Res..

[54]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[55]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[56]  D. Rumelhart.  Learning Internal Representations by Error Propagation, Parallel Distributed Processing , 1986 .

[57]  Ashwin Sanjay Neogi,et al.  Sentiment analysis and classification of Indian farmers' protest using twitter data , 2021, Int. J. Inf. Manag. Data Insights.