14.2.6.1 Continunal Learning, Incremental Learning

Chapter Contents (Back)
Contiunal Learning. Incremental Learning.

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


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.[Zilei], 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

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.[Jingwen], 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.[Bohyung], 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., Chen, L., Tung, F., He, J., Nawhal, M., Mori, G.,
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:Oct 19, 2020 at 15:02:28