14.2.6.1 Continunal Learning, Incremental Learning

Chapter Contents (Back)
Continual Learning. Active Learning. Incremental Learning. Online learning. Forgetting is one issue.
See also Forgetting, Explaination, Intrepretation, Understanding of Convolutional Neural Networks.

Hong, Y.[Yi], Kwong, S.[Sam], Chang, Y.C.[Yu-Chou], Ren, Q.S.[Qing-Sheng],
Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm,
PR(41), No. 9, September 2008, pp. 2742-2756.
Elsevier DOI 0806
Clustering ensembles; Dimensionality unbiased; Population based incremental learning algorithm; Unsupervised feature selection BibRef

Lughofer, E.[Edwin],
Extensions of vector quantization for incremental clustering,
PR(41), No. 3, March 2008, pp. 995-1011.
Elsevier DOI 0711
Vector quantization; Clustering; Incremental learning; New winning cluster selection strategy; Removing cluster satellites; Split-and-merge strategy; Image classification framework; Fault detection; Evolving fuzzy models BibRef

Jia, P.[Peng], Yin, J.S.[Jun-Song], Huang, X.S.[Xin-Sheng], Hu, D.[Dewen],
Incremental Laplacian eigenmaps by preserving adjacent information between data points,
PRL(30), No. 16, 1 December 2009, pp. 1457-1463.
Elsevier DOI 0911
Laplacian eigenmaps; Incremental learning; Locally linear construction; Nonlinear dimensionality reduction BibRef

Li, H.S.[Hou-Sen], Jiang, H.[Hao], Barrio, R.[Roberto], Liao, X.K.[Xiang-Ke], Cheng, L.Z.[Li-Zhi], Su, F.[Fang],
Incremental manifold learning by spectral embedding methods,
PRL(32), No. 10, 15 July 2011, pp. 1447-1455.
Elsevier DOI 1106
Manifold learning; Incremental learning; Dimensionality reduction; Spectral embedding methods; Hessian eigenmaps BibRef

Lu, G.F.[Gui-Fu], Jian, Z.[Zou], Wang, Y.[Yong],
Incremental learning from chunk data for IDR/QR,
IVC(36), No. 1, 2015, pp. 1-8.
Elsevier DOI 1504
Feature extraction incremental dimension reduction. BibRef

Le, T.B.[Thanh-Binh], Kim, S.W.[Sang-Woon],
On incrementally using a small portion of strong unlabeled data for semi-supervised learning algorithms,
PRL(41), No. 1, 2014, pp. 53-64.
Elsevier DOI 1403
Semi-supervised learning BibRef

Zhang, Z., Li, Y., Zhang, Z., Jin, C., Gao, M.,
Adaptive Matrix Sketching and Clustering for Semisupervised Incremental Learning,
SPLetters(25), No. 7, July 2018, pp. 1069-1073.
IEEE DOI 1807
learning (artificial intelligence), matrix algebra, pattern classification, adaptive matrix sketching, semisupervised classification BibRef

Li, Y.C.[Yan-Chao], Wang, Y.L.[Yong-Li], Liu, Q.[Qi], Bi, C.[Cheng], Jiang, X.H.[Xiao-Hui], Sun, S.R.[Shu-Rong],
Incremental semi-supervised learning on streaming data,
PR(88), 2019, pp. 383-396.
Elsevier DOI 1901
Semi-supervised learning, Dynamic feature learning, Streaming data, Classification BibRef

Besedin, A.[Andrey], Blanchart, P.[Pierre], Crucianu, M.[Michel], Ferecatu, M.[Marin],
Deep online classification using pseudo-generative models,
CVIU(201), 2020, pp. 103048.
Elsevier DOI 2011
Avoid issues of forgetting. Deep learning, Online learning, Pseudo-generative models, Stream learning BibRef

Peng, C.[Can], Zhao, K.[Kun], Lovell, B.C.[Brian C.],
Faster ILOD: Incremental learning for object detectors based on faster RCNN,
PRL(140), 2020, pp. 109-115.
Elsevier DOI 2012
Deep learning, Object detection, Incremental learning BibRef

Wang, G.X.[Guang-Xing], Ren, P.[Peng],
Hyperspectral Image Classification with Feature-Oriented Adversarial Active Learning,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Xiang, S.C.[Sun-Cheng], Fu, Y.Z.[Yu-Zhuo], Liu, T.[Ting],
Progressive learning with style transfer for distant domain adaptation,
IET-IPR(14), No. 14, December 2020, pp. 3527-3535.
DOI Link 2012
BibRef

Berger, K.[Katja], Caicedo, J.P.R.[Juan Pablo Rivera], Martino, L.[Luca], Wocher, M.[Matthias], Hank, T.[Tobias], Verrelst, J.[Jochem],
A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
Survey, Active Learning. BibRef

Li, J.[Jia], Song, Y.F.[Ya-Fei], Zhu, J.F.[Jian-Feng], Cheng, L.L.[Le-Le], Su, Y.[Ying], Ye, L.[Lin], Yuan, P.C.[Peng-Cheng], Han, S.M.[Shu-Min],
Learning From Large-Scale Noisy Web Data With Ubiquitous Reweighting for Image Classification,
PAMI(43), No. 5, May 2021, pp. 1808-1814.
IEEE DOI 2104
Noise measurement, Deep learning, Task analysis, Training, Annotations, Solid modeling, Visualization, Image classification, deep learning BibRef

Gweon, H.[Hyukjun], Yu, H.[Hao],
A nearest neighbor-based active learning method and its application to time series classification,
PRL(146), 2021, pp. 230-236.
Elsevier DOI 2105
Active learning, Batch mode, Time series classification, Nearest neighbor BibRef

Wang, Y.[Yi], Ding, Y.[Yi], He, X.J.[Xiang-Jian], Fan, X.[Xin], Lin, C.[Chi], Li, F.Q.[Feng-Qi], Wang, T.Z.[Tian-Zhu], Luo, Z.X.[Zhong-Xuan], Luo, J.B.[Jie-Bo],
Novelty Detection and Online Learning for Chunk Data Streams,
PAMI(43), No. 7, July 2021, pp. 2400-2412.
IEEE DOI 2106
Kernel, Data models, Linear systems, Fans, Hilbert space, Streaming media, Feature extraction, Data stream, online learning BibRef

Fonseca, J.[Joao], Douzas, G.[Georgios], Bacao, F.[Fernando],
Increasing the Effectiveness of Active Learning: Introducing Artificial Data Generation in Active Learning for Land Use/Land Cover Classification,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef


Roy, S.[Soumya], Sau, B.B.[Bharat Bhusan],
Can Selfless Learning improve accuracy of a single classification task?,
WACV21(4043-4051)
IEEE DOI 2106
solve the problem of catastrophic forgetting in continual learning. Training, Neurons, Task analysis BibRef

Bagi, A.M.[Alexandra M.], Schild, K.I.[Kim I.], Khan, O.S.[Omar Shahbaz], Zahálka, J.[Jan], Jónsson, B.Þ.[Björn Þór],
XQM: Interactive Learning on Mobile Phones,
MMMod21(II:281-293).
Springer DOI 2106
BibRef

Shi, F.F.[Fei-Fei], Wang, P.[Peng], Shi, Z.[Zhongchao], Rui, Y.[Yong],
Selecting Useful Knowledge from Previous Tasks for Future Learning in a Single Network,
ICPR21(9727-9732)
IEEE DOI 2105
Knowledge engineering, Learning systems, Network architecture, Iterative methods, Task analysis BibRef

Jarboui, F.[Firas], Perchet, V.[Vianney],
Trajectory representation learning for Multi-Task NMRDP planning,
ICPR21(6786-6793)
IEEE DOI 2105
Non Markovian Reward Decision Processes. Bridges, Reinforcement learning, Markov processes, Trajectory, Planning, Task analysis BibRef

Lechat, A.[Alexis], Herbin, S.[Stéphane], Jurie, F.[Frédéric],
Semi-Supervised Class Incremental Learning,
ICPR21(10383-10389)
IEEE DOI 2105
Training, Protocols, Image reconstruction BibRef

Chang, X.Y.[Xin-Yuan], Tao, X.Y.[Xiao-Yu], Hong, X.P.[Xiao-Peng], Wei, X.[Xing], Ke, W.[Wei], Gong, Y.H.[Yi-Hong],
Class-Incremental Learning with Topological Schemas of Memory Spaces,
ICPR21(9719-9726)
IEEE DOI 2105
Multiprotocol label switching, Manifolds, Knowledge engineering, Adaptation models, Network topology, Neural networks, Topological Schemas Model BibRef

Pernici, F.[Federico], Bruni, M.[Matteo], Baecchi, C.[Claudio], Turchini, F.[Francesco], del Bimbo, A.[Alberto],
Class-incremental Learning with Pre-allocated Fixed Classifiers,
ICPR21(6259-6266)
IEEE DOI 2105
Training, Knowledge engineering, Neural networks, Standards, Faces BibRef

Buzzega, P.[Pietro], Boschini, M.[Matteo], Porrello, A.[Angelo], Calderara, S.[Simone],
Rethinking Experience Replay: a Bag of Tricks for Continual Learning,
ICPR21(2180-2187)
IEEE DOI 2105
Degradation, Neural networks, Proposals, Erbium, Standards BibRef

Fidalgo-Merino, R.[Raúl], Gabrielli, L.[Lorenzo], Checchi, E.[Enrico],
Leveraging Sequential Pattern Information for Active Learning from Sequential Data,
ICPR21(6957-6964)
IEEE DOI 2105
Training, Machine learning algorithms, Annotations, Databases, Manuals, Machine learning, Data models BibRef

Agarwal, A.[Arvind], Mujumdar, S.[Shashank], Gupta, N.[Nitin], Mehta, S.[Sameep],
Budgeted Batch Mode Active Learning with Generalized Cost and Utility Functions,
ICPR21(7692-7698)
IEEE DOI 2105
Learning systems, Training data, Cost function, Data models, Labeling BibRef

Herde, M.[Marek], Kottke, D.[Daniel], Huseljic, D.[Denis], Sick, B.[Bernhard],
Multi-Annotator Probabilistic Active Learning,
ICPR21(10281-10288)
IEEE DOI 2105
Training, Deep learning, Annotations, Computational modeling, Employment, Manuals, Gaussian processes BibRef

Arnavaz, K.[Kasra], Feragen, A.[Aasa], Krause, O.[Oswin], Loog, M.[Marco],
Bayesian Active Learning for Maximal Information Gain on Model Parameters,
ICPR21(10524-10531)
IEEE DOI 2105
Machine learning, Data models, Bayes methods, Logistics BibRef

Li, M.[Minghan], Liu, X.[Xialei], van de Weijer, J.[Joost], Raducanu, B.[Bogdan],
Learning to Rank for Active Learning: A Listwise Approach,
ICPR21(5587-5594)
IEEE DOI 2105
Training, Measurement, Correlation, Prediction algorithms, Classification algorithms, Labeling BibRef

Siméoni, O.[Oriane], Budnik, M.[Mateusz], Avrithis, Y.[Yannis], Gravier, G.[Guillaume],
Rethinking deep active learning: Using unlabeled data at model training,
ICPR21(1220-1227)
IEEE DOI 2105
Training, Deep learning, Pipelines, Semisupervised learning, Data models, Image classification BibRef

Li, C.[Cheng], Rana, S.[Santu], Gill, A.[Andrew], Nguyen, D.[Dang], Gupta, S.I.[Sun-Il], Venkatesh, S.[Svetha],
Factor Screening using Bayesian Active Learning and Gaussian Process Meta-Modelling,
ICPR21(3288-3295)
IEEE DOI 2105
Gaussian processes, Length measurement, Entropy, Bayes methods, Kernel, Factor screening, Gaussian Process BibRef

Li, X.O.[Xia-Obin], Shan, L.[Lianlei], Li, M.[Minglong], Wang, W.Q.[Wei-Qiang],
Energy Minimum Regularization in Continual Learning,
ICPR21(6404-6409)
IEEE DOI 2105
Learning systems, Sensitivity, Animals, Solids, Minimization, Pattern recognition, Task analysis BibRef

Ho, C.H.[Chih-Hsing], Tsai, S.H.L.[Shang-Ho Lawrence],
RSAC: Regularized Subspace Approximation Classifier for Lightweight Continuous Learning,
ICPR21(6680-6687)
IEEE DOI 2105
Training, Memory management, Training data, Approximation algorithms, Classification algorithms, Streaming Learning BibRef

Lei, C.H.[Cheng-Hsun], Chen, Y.H.[Yi-Hsin], Peng, W.H.[Wen-Hsiao], Chiu, W.C.[Wei-Chen],
Class-Incremental Learning with Rectified Feature-Graph Preservation,
ACCV20(VI:358-374).
Springer DOI 2103
Learn new classes as they arrive. BibRef

Chan, D.M.[David M.], Vijayanarasimhan, S.[Sudheendra], Ross, D.A.[David A.], Canny, J.F.[John F.],
Active Learning for Video Description with Cluster-regularized Ensemble Ranking,
ACCV20(V:443-459).
Springer DOI 2103
BibRef

Wang, S.[Shuo], Li, Y.X.[Yue-Xiang], Ma, K.[Kai], Ma, R.[Ruhui], Guan, H.B.[Hai-Bing], Zheng, Y.F.[Ye-Feng],
Dual Adversarial Network for Deep Active Learning,
ECCV20(XXIV:680-696).
Springer DOI 2012
BibRef

Lin, Z.[Zudi], Wei, D.[Donglai], Jang, W.D.[Won-Dong], Zhou, S.[Siyan], Chen, X.[Xupeng], Wang, X.[Xueying], Schalek, R.[Richard], Berger, D.[Daniel], Matejek, B.[Brian], Kamentsky, L.[Lee], Peleg, A.[Adi], Haehn, D.[Daniel], Jones, T.[Thouis], Parag, T.[Toufiq], Lichtman, J.[Jeff], Pfister, H.[Hanspeter],
Two Stream Active Query Suggestion for Active Learning in Connectomics,
ECCV20(XVIII:103-120).
Springer DOI 2012
BibRef

Ebrahimi, S.[Sayna], Meier, F.[Franziska], Calandra, R.[Roberto], Darrell, T.J.[Trevor J.], Rohrbach, M.[Marcus],
Adversarial Continual Learning,
ECCV20(XI:386-402).
Springer DOI 2011
BibRef

Kim, C.D.[Chris Dongjoo], Jeong, J.[Jinseo], Kim, G.[Gunhee],
Imbalanced Continual Learning with Partitioning Reservoir Sampling,
ECCV20(XIII:411-428).
Springer DOI 2011
BibRef

Kim, E.S., Kim, J.U., Lee, S., Moon, S.K., Ro, Y.M.,
Class Incremental Learning With Task-Selection,
ICIP20(1846-1850)
IEEE DOI 2011
Task analysis, Learning systems, Image reconstruction, Feature extraction, Training, Testing, Data models, Deep learning, autoencoder BibRef

Yao, X., Sun, L.,
Continual Local Training For Better Initialization Of Federated Models,
ICIP20(1736-1740)
IEEE DOI 2011
Training, Data models, Servers, Task analysis, Computational modeling, Distributed databases, Optimization, Generalization BibRef

Gao, M.F.[Ming-Fei], Zhang, Z.Z.[Zi-Zhao], Yu, G.[Guo], Arik, S.Ö.[Sercan Ö.], Davis, L.S.[Larry S.], Pfister, T.[Tomas],
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost,
ECCV20(X:510-526).
Springer DOI 2011
BibRef

Chaplot, D.S.[Devendra Singh], Jiang, H.[Helen], Gupta, S.[Saurabh], Gupta, A.[Abhinav],
Semantic Curiosity for Active Visual Learning,
ECCV20(VI:309-326).
Springer DOI 2011
BibRef

Fini, E.[Enrico], Lathuilière, S.[Stéphane], Sangineto, E.[Enver], Nabi, M.[Moin], Ricci, E.[Elisa],
Online Continual Learning Under Extreme Memory Constraints,
ECCV20(XXVIII:720-735).
Springer DOI 2011
BibRef

Agarwal, S.[Sharat], Arora, H.[Himanshu], Anand, S.[Saket], Arora, C.[Chetan],
Contextual Diversity for Active Learning,
ECCV20(XVI: 137-153).
Springer DOI 2010
BibRef

Iscen, A.[Ahmet], Zhang, J.[Jeffrey], Lazebnik, S.[Svetlana], Schmid, C.[Cordelia],
Memory-efficient Incremental Learning Through Feature Adaptation,
ECCV20(XVI: 699-715).
Springer DOI 2010
BibRef

Yu, L., Twardowski, B., Liu, X., Herranz, L., Wang, K., Cheng, Y., Jui, S., van de Weijer, J.,
Semantic Drift Compensation for Class-Incremental Learning,
CVPR20(6980-6989)
IEEE DOI 2008
Task analysis, Training, Prototypes, Semantics, Measurement, Neurons, Computer vision BibRef

Zhao, B., Xiao, X., Gan, G., Zhang, B., Xia, S.,
Maintaining Discrimination and Fairness in Class Incremental Learning,
CVPR20(13205-13214)
IEEE DOI 2008
Training, Task analysis, Data models, Error analysis, Neural networks, Standards BibRef

Rajasegaran, J., Khan, S., Hayat, M., Khan, F.S., Shah, M.,
iTAML: An Incremental Task-Agnostic Meta-learning Approach,
CVPR20(13585-13594)
IEEE DOI 2008
Task analysis, Adaptation models, Training, Stability analysis, Interference, Predictive models, Heuristic algorithms BibRef

He, J., Mao, R., Shao, Z., Zhu, F.,
Incremental Learning in Online Scenario,
CVPR20(13923-13932)
IEEE DOI 2008
Data models, Machine learning, Training, Task analysis, Feature extraction, Predictive models, Learning systems BibRef

Mi, F., Kong, L., Lin, T., Yu, K., Faltings, B.,
Generalized Class Incremental Learning,
CLVision20(970-974)
IEEE DOI 2008
Erbium, Training, Data models, Computational modeling, Probabilistic logic, Machine learning, Task analysis BibRef

Ayub, A., Wagner, A.R.,
Cognitively-Inspired Model for Incremental Learning Using a Few Examples,
CLVision20(897-906)
IEEE DOI 2008
Feature extraction, Task analysis, Training, Machine learning, Training data, Data models, Hippocampus BibRef

Hayes, T.L., Kanan, C.,
Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis,
CLVision20(887-896)
IEEE DOI 2008
Streaming media, Covariance matrices, Training, Computational modeling, Neural networks, Task analysis, Linear discriminant analysis BibRef

Dhar, P.[Prithviraj], Singh, R.V.[Rajat Vikram], Peng, K.C.[Kuan-Chuan], Wu, Z.[Ziyan], Chellappa, R.[Rama],
Learning Without Memorizing,
CVPR19(5133-5141).
IEEE DOI 2002
Incremental learning, but can't store the whole past. BibRef

Hou, S.H.[Sai-Hui], Pan, X.Y.[Xin-Yu], Loy, C.C.[Chen Change], Wang, Z.L.[Zi-Lei], Lin, D.H.[Da-Hua],
Learning a Unified Classifier Incrementally via Rebalancing,
CVPR19(831-839).
IEEE DOI 2002
BibRef

Belouadah, E.[Eden], Popescu, A.[Adrian],
DeeSIL: Deep-Shallow Incremental Learning,
TASKCV18(II:151-157).
Springer DOI 1905
BibRef

Castro, F.M.[Francisco M.], Marín-Jiménez, M.J.[Manuel J.], Guil, N.[Nicolás], Schmid, C.[Cordelia], Alahari, K.[Karteek],
End-to-End Incremental Learning,
ECCV18(XII: 241-257).
Springer DOI 1810
BibRef

Prabhu, A.[Ameya], Torr, P.H.S.[Philip H. S.], Dokania, P.K.[Puneet K.],
GDUMB: A Simple Approach that Questions Our Progress in Continual Learning,
ECCV20(II:524-540).
Springer DOI 2011
BibRef

Chaudhry, A.[Arslan], Dokania, P.K.[Puneet K.], Ajanthan, T.[Thalaiyasingam], Torr, P.H.S.[Philip H. S.],
Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence,
ECCV18(XI: 556-572).
Springer DOI 1810
BibRef

Lomonaco, V., Maltoni, D., Pellegrini, L.,
Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches,
CLVision20(989-998)
IEEE DOI 2008
Training, Robots, Videos, Object recognition, Benchmark testing, Computer architecture, Computational modeling BibRef

Silver, D.L., Mahfuz, S.,
Generating Accurate Pseudo Examples for Continual Learning,
CLVision20(1035-1042)
IEEE DOI 2008
Task analysis, Training, Probability distribution, Knowledge engineering, Input variables, Neural networks BibRef

Parshotam, K., Kilickaya, M.,
Continual Learning of Object Instances,
CLVision20(907-914)
IEEE DOI 2008
Automobiles, Task analysis, Measurement, Training, Data models, Visualization, Companies BibRef

Liu, X., Wu, C., Menta, M., Herranz, L., Raducanu, B., Bagdanov, A.D., Jui, S., van de Weijer, J.,
Generative Feature Replay For Class-Incremental Learning,
CLVision20(915-924)
IEEE DOI 2008
Task analysis, Feature extraction, Image generation, Correlation, Training, Generators BibRef

Mirzadeh, S.I., Farajtabar, M., Ghasemzadeh, H.,
Dropout as an Implicit Gating Mechanism For Continual Learning,
CLVision20(945-951)
IEEE DOI 2008
Task analysis, Neurons, Stability analysis, Training, Standards, Logic gates, Knowledge engineering BibRef

Liu, Y., Su, Y., Liu, A., Schiele, B., Sun, Q.,
Mnemonics Training: Multi-Class Incremental Learning Without Forgetting,
CVPR20(12242-12251)
IEEE DOI 2008
Training, Optimization, Data models, Computational modeling, Generative adversarial networks, Training data BibRef

Zhang, J.[Jie], Zhang, J.T.[Jun-Ting], Ghosh, S.[Shalini], Li, D.[Dawei], Zhu, J.W.[Jing-Wen], Zhang, H.M.[He-Ming], Wang, Y.L.[Ya-Lin],
Regularize, Expand and Compress: NonExpansive Continual Learning,
WACV20(843-851)
IEEE DOI 2006
Task analysis, Computational modeling, Computer architecture, Network architecture, Neural networks, Knowledge engineering, Correlation BibRef

Belouadah, E.[Eden], Popescu, A.[Adrian],
ScaIL: Classifier Weights Scaling for Class Incremental Learning,
WACV20(1255-1264)
IEEE DOI 2006
BibRef
Earlier:
IL2M: Class Incremental Learning With Dual Memory,
ICCV19(583-592)
IEEE DOI 2004
Tuning, Adaptation models, Training, Feature extraction, Machine learning, Memory management, Task analysis. computational complexity, image classification, inference mechanisms, learning (artificial intelligence), Computer architecture BibRef

Ostapenko, O.[Oleksiy], Puscas, M.[Mihai], Klein, T.[Tassilo], Jahnichen, P.[Patrick], Nabi, M.[Moin],
Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning,
CVPR19(11313-11321).
IEEE DOI 2002
BibRef

Stojanov, S.[Stefan], Mishra, S.[Samarth], Thai, N.A.[Ngoc Anh], Dhanda, N.[Nikhil], Humayun, A.[Ahmad], Yu, C.[Chen], Smith, L.B.[Linda B.], Rehg, J.M.[James M.],
Incremental Object Learning From Contiguous Views,
CVPR19(8769-8778).
IEEE DOI 2002
BibRef

Nwe, T.L., Nataraj, B., Shudong, X., Yiqun, L., Dongyun, L., Sheng, D.,
Discriminative Features for Incremental Learning Classifier,
ICIP19(1990-1994)
IEEE DOI 1910
Incremental learning, Context Aware Advertisement, Few-short incremental learning, Discriminative features, Catastrophic forgetting BibRef

Murata, K.[Kengo], Toyota, T.[Tetsuya], Ohara, K.[Kouzou],
What is Happening Inside a Continual Learning Model?: A Representation-Based Evaluation of Representational Forgetting,
CLVision20(952-956)
IEEE DOI 2008
Task analysis, Erbium, Measurement, Learning systems, Standards, Neural networks, Data models BibRef

Abati, D., Tomczak, J., Blankevoort, T., Calderara, S., Cucchiara, R., Bejnordi, B.E.,
Conditional Channel Gated Networks for Task-Aware Continual Learning,
CVPR20(3930-3939)
IEEE DOI 2008
Task analysis, Logic gates, Training, Computational modeling, Neural networks, Machine learning, Computer architecture BibRef

Lee, J., Hong, H.G., Joo, D., Kim, J.,
Continual Learning With Extended Kronecker-Factored Approximate Curvature,
CVPR20(8998-9007)
IEEE DOI 2008
Task analysis, Neural networks, Mathematical model, Learning systems, Optimization, Network architecture, Training BibRef

Kim, J., Kim, J., Kwak, N.,
StackNet: Stacking feature maps for Continual learning,
CLVision20(975-982)
IEEE DOI 2008
Task analysis, Indexes, Training, Data models, Biological neural networks, Stacking, Machine learning BibRef

Du, X., Li, Z., Seo, J., Liu, F., Cao, Y.,
Noise-based Selection of Robust Inherited Model for Accurate Continual Learning,
CLVision20(983-988)
IEEE DOI 2008
Conferences, Computer vision, Pattern recognition BibRef

Lomonaco, V., Desai, K., Culurciello, E., Maltoni, D.,
Continual Reinforcement Learning in 3D Non-stationary Environments,
CLVision20(999-1008)
IEEE DOI 2008
Task analysis, Learning (artificial intelligence), Benchmark testing, Color, Training, Complexity theory BibRef

Aljundi, R.[Rahaf], Kelchtermans, K.[Klaas], Tuytelaars, T.[Tinne],
Task-Free Continual Learning,
CVPR19(11246-11255).
IEEE DOI 2002
BibRef

Park, D.M.[Dong-Min], Hong, S.[Seokil], Han, B.H.[Bo-Hyung], Lee, K.M.[Kyoung Mu],
Continual Learning by Asymmetric Loss Approximation With Single-Side Overestimation,
ICCV19(3334-3343)
IEEE DOI 2004
function approximation, learning (artificial intelligence), neural nets, asymmetric loss approximation, Scalability BibRef

El Khatib, A.[Alaa], Karray, F.[Fakhri],
Strategies for Improving Single-Head Continual Learning Performance,
ICIAR19(I:452-460).
Springer DOI 1909
Forgetting. Problem is also not all data is available at once. BibRef

Hayes, T.L., Kemker, R., Cahill, N.D., Kanan, C.,
New Metrics and Experimental Paradigms for Continual Learning,
DeepLearnRV18(2112-21123)
IEEE DOI 1812
Robots, Measurement, Training, Task analysis, Computational modeling, Neural networks, Data models BibRef

Zhai, M.Y.[Meng-Yao], Chen, L.[Lei], He, J.W.[Jia-Wei], Nawhal, M.[Megha], Tung, F.[Frederick], Mori, G.[Greg],
Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation,
ECCV20(XXI:397-413).
Springer DOI 2011
BibRef
Earlier: A1, A2, A5, A3, A4, A6:
Lifelong GAN: Continual Learning for Conditional Image Generation,
ICCV19(2759-2768)
IEEE DOI 2004
image processing, learning (artificial intelligence), neural nets, continual learning, deep neural networks, Training data BibRef

Lopes, N.[Noel], Ribeiro, B.[Bernardete],
Trading off Distance Metrics vs Accuracy in Incremental Learning Algorithms,
CIARP16(530-538).
Springer DOI 1703
BibRef
Earlier:
On the Impact of Distance Metrics in Instance-Based Learning Algorithms,
IbPRIA15(48-56).
Springer DOI 1506
BibRef

Ditzler, G.[Gregory], Polikar, R.[Robi], Chawla, N.V.[Nitesh V.],
An Incremental Learning Algorithm for Non-stationary Environments and Class Imbalance,
ICPR10(2997-3000).
IEEE DOI 1008
BibRef

Almaksour, A.[Abdullah], Anquetil, E.[Eric], Quiniou, S.[Solen], Cheriet, M.[Mohamed],
Evolving Fuzzy Classifiers: Application to Incremental Learning of Handwritten Gesture Recognition Systems,
ICPR10(4056-4059).
IEEE DOI 1008
BibRef

Sudo, K.[Kyoko], Osawa, T.[Tatsuya], Tanaka, H.[Hidenori], Koike, H.[Hideki], Arakawa, K.[Kenichi],
Online anomal movement detection based on unsupervised incremental learning,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Zhang, R.[Rong], Rudnicky, A.I.[Alexander I.],
A New Data Selection Principle for Semi-Supervised Incremental Learning,
ICPR06(II: 780-783).
IEEE DOI 0609
BibRef

Prehn, H.[Herward], Sommer, G.[Gerald],
An Adaptive Classification Algorithm Using Robust Incremental Clustering,
ICPR06(I: 896-899).
IEEE DOI 0609
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Subspace Clustering, Subspace Learning .


Last update:Jul 11, 2021 at 20:18:24