14.1.10.5 Hallucination for Few Shot Learning, Augmentation

Zhang, L.[Lei], Zhou, F.[Fei], Wei, W.[Wei], Zhang, Y.N.[Yan-Ning],
Meta-Hallucinating Prototype for Few-Shot Learning Promotion,
PR(136), 2023, pp. 109235.
Elsevier DOI 2301
Few-shot learning, Prototype hallucination, Meta-learning BibRef

Lin, C.C.[Chia-Ching], Chu, H.L.[Hsin-Li], Wang, Y.C.A.F.[Yu-Chi-Ang Frank], Lei, C.L.[Chin-Laung],
Joint Feature Disentanglement and Hallucination for Few-Shot Image Classification,
IP(30), 2021, pp. 9245-9258.
IEEE DOI 2112
Task analysis, Feature extraction, Visualization, Training, Data models, Data mining, Birds, Few-shot learning (FSL), feature disentanglement BibRef

Yang, S.[Sai], Liu, F.[Fan], Chen, Z.Y.[Zhi-Yu],
Feature hallucination in hypersphere space for few-shot classification,
IET-IPR(16), No. 13, 2022, pp. 3603-3616.
DOI Link 2210
BibRef

Hu, Z.X.[Zi-Xuan], Shen, L.[Li], Lai, S.[Shenqi], Yuan, C.[Chun],
Task-Adaptive Feature Disentanglement and Hallucination for Few-Shot Classification,
CirSysVideo(33), No. 8, August 2023, pp. 3638-3648.
IEEE DOI 2308
Task analysis, Bayes methods, Frequency division multiplexing, Correlation, Uncertainty, Prototypes, Semantics, Bayesian inference BibRef

Wu, S.N.[Si-Ning], Gao, X.[Xiang], Hu, X.P.[Xiao-Peng],
Task-Oriented Feature Hallucination for Few-Shot Image Classification,
IET-IPR(17), No. 12, 2023, pp. 3564-3579.
DOI Link 2310
image classification, image recognition, image representation, pattern recognition, supervised learning BibRef

Deshpande, R.[Rucha], Anastasio, M.A.[Mark A.], Brooks, F.J.[Frank J.],
A method for evaluating deep generative models of images for hallucinations in high-order spatial context,
PRL(186), 2024, pp. 23-29.
Elsevier DOI 2412
Deep generative model evaluation, Hallucination, Stochastic context models, Benchmark, Dataset, Generative adversarial networks BibRef

Wu, H.[Hefeng], Ye, G.Z.[Guang-Zhi], Zhou, Z.Y.[Zi-Yang], Tian, L.[Ling], Wang, Q.[Qing], Lin, L.[Liang],
Dual-View Data Hallucination With Semantic Relation Guidance for Few-Shot Image Recognition,
MultMed(26), 2024, pp. 11302-11315.
IEEE DOI 2412
Semantics, Visualization, Prototypes, Task analysis, Data models, Image recognition, Few shot learning, Few-shot learning. BibRef

Lazarou, M.[Michalis], Stathaki, T.[Tania], Avrithis, Y.[Yannis],
Tensor feature hallucination for few-shot learning,
WACV22(2050-2060)
IEEE DOI 2202
Training, Representation learning, Tensors, Focusing, Performance gain, Generative adversarial networks, GANs BibRef

Li, K., Zhang, Y., Li, K., Fu, Y.,
Adversarial Feature Hallucination Networks for Few-Shot Learning,
CVPR20(13467-13476)
IEEE DOI 2008
Generators, Task analysis, Data models, Training, Measurement, Neural networks BibRef

Zhang, H.G.[Hong-Guang], Zhang, J.[Jing], Koniusz, P.[Piotr],
Few-Shot Learning via Saliency-Guided Hallucination of Samples,
CVPR19(2765-2774).
IEEE DOI 2002
BibRef

Pahde, F.[Frederik], Nabi, M.[Main], Klein, T.[Tassila], Jahnichen, P.[Patrick],
Discriminative Hallucination for Multi-Modal Few-Shot Learning,
ICIP18(156-160)
IEEE DOI 1809
Training, Visualization, Birds, Machine learning, Training data, Task analysis, Few-Shot Learning, Multi-Modal, Fine-grained Recognition BibRef

Hariharan, B.[Bharath], Girshick, R.[Ross],
Low-Shot Visual Recognition by Shrinking and Hallucinating Features,
ICCV17(3037-3046)
IEEE DOI 1802
Recognize categories from very few examples. image recognition, learning (artificial intelligence), object recognition, feature hallucination, feature shrinking, Visualization BibRef

Luo, Q.X.[Qin-Xuan], Wang, L.F.[Ling-Feng], Lv, J.[Jingguo], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Few-Shot Learning via Feature Hallucination with Variational Inference,
WACV21(3962-3971)
IEEE DOI 2106
Training, Deep learning, Computational modeling, Gaussian distribution, Data models BibRef

Chu, W., Wang, Y.F.,
Learning Semantics-Guided Visual Attention for Few-Shot Image Classification,
ICIP18(2979-2983)
IEEE DOI 1809
Task analysis, Training, Feature extraction, Visualization, Semantics, Generators, Silicon, Few-shot learning, image classification BibRef

Lin, C., Wang, Y.F., Lei, C., Chen, K.,
Semantics-Guided Data Hallucination for Few-Shot Visual Classification,
ICIP19(3302-3306)
IEEE DOI 1910
Few-shot learning, deep learning, image classification, data hallucination BibRef

Chiaroni, F., Rahal, M., Hueber, N., Dufaux, F.,
Hallucinating A Cleanly Labeled Augmented Dataset from A Noisy Labeled Dataset Using GAN,
ICIP19(3616-3620)
IEEE DOI 1910
Generative adversarial networks, noisy labeled learning, image classification BibRef

Carlucci, F.M., Russo, P., Tommasi, T., Caputo, B.,
Hallucinating Agnostic Images to Generalize Across Domains,
TASKCV19(3227-3234)
IEEE DOI 2004
image classification, learning (artificial intelligence), adversarial domain classifier, unlabeled target samples, multisource domain adaptation BibRef

Zhang, W.H.[Wei-He], Wang, Y.[Yali], Qiao, Y.[Yu],
MetaCleaner: Learning to Hallucinate Clean Representations for Noisy-Labeled Visual Recognition,
CVPR19(7365-7374).
IEEE DOI 2002
BibRef

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