13.6.3.4 Fine Tuning, Fine-Tuning, Pre-Training, Zero-Shot, One-Shot

Chapter Contents (Back)
Fine Tuning. Pre-Training. Zero-Shot Learning. Few-Shot Learning.
See also Context, Fine-Grained Classification.
See also Pre-Training.
See also Zero-Shot Learning.
See also Few Shot Learning.

Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J.,
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?,
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IEEE DOI 1605
Biomedical imaging BibRef

Shin, J.Y., Tajbakhsh, N., Hurst, R.T., Kendall, C.B., Liang, J.,
Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks,
CVPR16(2526-2535)
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Zhou, Z., Shin, J., Zhang, L., Gurudu, S., Gotway, M.B., Liang, J.,
Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally,
CVPR17(4761-4772)
IEEE DOI 1711
Biomedical imaging, Entropy, Labeling, Machine learning, Noise measurement, Training BibRef

Zheng, Y.[Yan], Wang, R.G.[Rong-Gui], Yang, J.[Juan], Xue, L.X.[Li-Xia], Hu, M.[Min],
Principal characteristic networks for few-shot learning,
JVCIR(59), 2019, pp. 563-573.
Elsevier DOI 1903
Few-shot learning, Principal characteristic, Mixture loss function, Embedding network, Fine-tuning BibRef

Yang, H.D.[Hao-Dong], Kang, X.Y.[Xin-Yue], Liu, L.[Long], Liu, Y.J.[Yu-Jiang], Huang, Z.L.[Zhong-Ling],
SAR-HUB: Pre-Training, Fine-Tuning, and Explaining,
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Li, X.[Xiao], Fang, M.[Min], Li, H.[Haikun], Wu, J.Q.[Jin-Qiao],
Zero shot learning based on class visual prototypes and semantic consistency,
PRL(135), 2020, pp. 368-374.
Elsevier DOI 2006
Zero shot learning, Semantic consistency, Class visual prototypes, Shared sparse graph BibRef

Li, X.[Xiao], Fang, M.[Min], Li, H.[Haikun], Wu, J.Q.[Jin-Qiao],
Learning discriminative and meaningful samples for generalized zero shot classification,
SP:IC(87), 2020, pp. 115920.
Elsevier DOI 2007
Generalized zero shot classification, Generative adversarial network, Class consistency, Semantic consistency BibRef

Li, X.[Xiao], Fang, M.[Min], Chen, B.[Bo],
Generalized zero-shot classification via iteratively generating and selecting unseen samples,
SP:IC(92), 2021, pp. 116115.
Elsevier DOI 2101
Generalized zero shot classification, Generative adversarial network, Unseen visual prototypes BibRef

Zhai, Z.B.[Zhi-Bo], Li, X.[Xiao], Chang, Z.H.[Zhong-Hao],
Center-VAE with discriminative and semantic-relevant fine-tuning features for generalized zero-shot learning,
SP:IC(111), 2023, pp. 116897.
Elsevier DOI 2301
Generalized zero-shot learning, VAE, Center loss, Semantic-relevant, Fine-tuning BibRef

Pang, S.[Shanmin], He, X.[Xin], Hao, W.Y.[Wen-Yu], Long, Y.[Yang],
Feature fine-tuning and attribute representation transformation for zero-shot learning,
CVIU(236), 2023, pp. 103811.
Elsevier DOI 2310
Generalized zero-shot learning, Generative adversarial networks, Data distribution, Information asymmetric problem BibRef

Mao, X.F.[Xiao-Feng], Chen, Y.F.[Yu-Feng], Jia, X.J.[Xiao-Jun], Zhang, R.[Rong], Xue, H.[Hui], Li, Z.[Zhao],
Context-Aware Robust Fine-Tuning,
IJCV(132), No. 5, May 2024, pp. 1685-1700.
Springer DOI 2405
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Goyal, S.[Sachin], Kumar, A.[Ananya], Garg, S.[Sankalp], Kolter, Z.[Zico], Raghunathan, A.[Aditi],
Finetune like you pretrain: Improved finetuning of zero-shot vision models,
CVPR23(19338-19347)
IEEE DOI 2309
BibRef

Han, C.[Cheng], Wang, Q.F.[Qi-Fan], Cui, Y.M.[Yi-Ming], Cao, Z.W.[Zhi-Wen], Wang, W.G.[Wen-Guan], Qi, S.Y.[Si-Yuan], Liu, D.F.[Dong-Fang],
E2VPT: An Effective and Efficient Approach for Visual Prompt Tuning,
ICCV23(17445-17456)
IEEE DOI Code:
WWW Link. 2401
BibRef

Jie, S.[Shibo], Wang, H.Q.[Hao-Qing], Deng, Z.H.[Zhi-Hong],
Revisiting the Parameter Efficiency of Adapters from the Perspective of Precision Redundancy,
ICCV23(17171-17180)
IEEE DOI Code:
WWW Link. 2401
BibRef

Yang, Y.Q.[Yun-Qiao], Huang, L.K.[Long-Kai], Wei, Y.[Ying],
Concept-wise Fine-tuning Matters in Preventing Negative Transfer,
ICCV23(18707-18717)
IEEE DOI 2401
BibRef

Li, H.[Hao], Fowlkes, C.[Charless], Yang, H.[Hao], Dabeer, O.[Onkar], Tu, Z.W.[Zhuo-Wen], Soatto, S.[Stefano],
Guided Recommendation for Model Fine-Tuning,
CVPR23(3633-3642)
IEEE DOI 2309
BibRef

Wang, J.Y.[Jun-Yang], Xu, Y.H.[Yuan-Hong], Hu, J.[Juhua], Yan, M.[Ming], Sang, J.[Jitao], Qian, Q.[Qi],
Improved Visual Fine-tuning with Natural Language Supervision,
ICCV23(11865-11875)
IEEE DOI Code:
WWW Link. 2401
BibRef

Tian, J.J.[Jun-Jiao], Dai, X.L.[Xiao-Liang], Ma, C.Y.[Chih-Yao], He, Z.C.[Ze-Cheng], Liu, Y.C.[Yen-Cheng], Kira, Z.[Zsolt],
Trainable Projected Gradient Method for Robust Fine-Tuning,
CVPR23(7836-7845)
IEEE DOI 2309
BibRef

Zhou, N.[Nan], Chen, J.X.[Jia-Xin], Huang, D.[Di],
DR-Tune: Improving Fine-tuning of Pretrained Visual Models by Distribution Regularization with Semantic Calibration,
ICCV23(1547-1556)
IEEE DOI Code:
WWW Link. 2401
BibRef

Singh, A.[Aaditya], Sarangmath, K.[Kartik], Chattopadhyay, P.[Prithvijit], Hoffman, J.[Judy],
Benchmarking Low-Shot Robustness to Natural Distribution Shifts,
ICCV23(16186-16196)
IEEE DOI 2401
fine-tuning BibRef

Xie, E.[Enze], Yao, L.W.[Le-Wei], Shi, H.[Han], Liu, Z.[Zhili], Zhou, D.[Daquan], Liu, Z.Q.[Zhao-Qiang], Li, J.W.[Jia-Wei], Li, Z.G.[Zhen-Guo],
DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning,
ICCV23(4207-4216)
IEEE DOI 2401
BibRef

Liu, T.Y.[Tian Yu], Soatto, S.[Stefano],
Tangent Model Composition for Ensembling and Continual Fine-tuning,
ICCV23(18630-18640)
IEEE DOI Code:
WWW Link. 2401
BibRef

Tao, R.[Ran], Chen, H.[Hao], Savvides, M.[Marios],
Boosting Transductive Few-Shot Fine-tuning with Margin-based Uncertainty Weighting and Probability Regularization,
CVPR23(15752-15761)
IEEE DOI 2309
BibRef

Shen, T.Y.[Tian-Yi], Lee, C.[Chonghan], Narayanan, V.[Vijaykrishnan],
Multi-Exit Vision Transformer with Custom Fine-Tuning for Fine-Grained Image Recognition,
ICIP23(2830-2834)
IEEE DOI 2312
BibRef

Liu, Z.Q.[Zi-Quan], Xu, Y.[Yi], Ji, X.Y.[Xiang-Yang], Chan, A.B.[Antoni B.],
TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization,
CVPR23(16436-16446)
IEEE DOI 2309
BibRef

Park, H.[Hojin], Park, J.[Jaewoo], Teoh, A.B.J.[Andrew Beng Jin],
Open-Set Face Identification on Few-Shot Gallery by Fine-Tuning,
ICPR22(1026-1032)
IEEE DOI 2212

WWW Link. Face recognition, Source coding, Computational modeling, Benchmark testing, Task analysis BibRef

Xu, H.[Hang], Kang, N.[Ning], Zhang, G.[Gengwei], Xie, C.L.[Chuan-Long], Liang, X.D.[Xiao-Dan], Li, Z.G.[Zhen-Guo],
NASOA: Towards Faster Task-Oriented Online Fine-Tuning with a Zoo of Models,
ICCV21(5077-5086)
IEEE DOI 2203
Training, Adaptation models, Cloud computing, Schedules, Computational modeling, Graphics processing units, Vision applications and systems BibRef

Chamand, B.[Benjamin], Risser-Maroix, O.[Olivier], Kurtz, C.[Camille], Joly, P.[Philippe], Loménie, N.[Nicolas],
Fine-Tune Your Classifier: Finding Correlations with Temperature,
ICIP22(2766-2770)
IEEE DOI 2211
Temperature measurement, Training, Measurement, Temperature distribution, Correlation, Computational modeling, cross-entropy BibRef

Shon, H.[Hyounguk], Lee, J.[Janghyeon], Kim, S.H.[Seung Hwan], Kim, J.[Junmo],
DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning,
ECCV22(XXXIII:513-529).
Springer DOI 2211
BibRef

Jie, S.[Shibo], Deng, Z.H.[Zhi-Hong], Li, Z.H.[Zi-Heng],
Alleviating Representational Shift for Continual Fine-tuning,
CLVision22(3809-3818)
IEEE DOI 2210
Training, Pattern recognition, Task analysis, Testing BibRef

Hu, S.X.[Shell Xu], Li, D.[Da], Stühmer, J.[Jan], Kim, M.Y.[Min-Young], Hospedales, T.M.[Timothy M.],
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference,
CVPR22(9058-9067)
IEEE DOI 2210
Training, Pipelines, Transfer learning, Benchmark testing, Transformers, Self- semi- meta- unsupervised learning BibRef

Wortsman, M.[Mitchell], Ilharco, G.[Gabriel], Kim, J.W.[Jong Wook], Li, M.[Mike], Kornblith, S.[Simon], Roelofs, R.[Rebecca], Lopes, R.G.[Raphael Gontijo], Hajishirzi, H.[Hannaneh], Farhadi, A.[Ali], Namkoong, H.[Hongseok], Schmidt, L.[Ludwig],
Robust fine-tuning of zero-shot models,
CVPR22(7949-7961)
IEEE DOI 2210
Computational modeling, Computer network reliability, Transfer learning, Neural networks, Robustness, Data models, Machine learning BibRef

Zhang, L.[Lin], Shen, L.[Li], Ding, L.[Liang], Tao, D.C.[Da-Cheng], Duan, L.Y.[Ling-Yu],
Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning,
CVPR22(10164-10173)
IEEE DOI 2210
Training, Privacy, Distance learning, Computational modeling, Machine learning, Collaborative work, Generators, Transfer/low-shot/long-tail learning BibRef

Cao, Y.T.[Yu-Tong], Shi, Y.[Ye], Yu, B.S.[Bao-Sheng], Wang, J.Y.[Jing-Ya], Tao, D.C.[Da-Cheng],
Knowledge-Aware Federated Active Learning with Non-IID Data,
ICCV23(22222-22232)
IEEE DOI Code:
WWW Link. 2401
BibRef

Suzuki, S.[Satoshi], Takeda, S.[Shoichiro], Tanida, R.[Ryuichi], Kimata, H.[Hideaki], Shouno, H.[Hayaru],
Knowledge Transferred Fine-Tuning for Anti-Aliased Convolutional Neural Network in Data-Limited Situation,
ICIP21(864-868)
IEEE DOI 2201
Knowledge engineering, Training, Image recognition, Training data, Convolutional neural networks, Knowledge transfer, data-limited situation BibRef

Achille, A.[Alessandro], Golatkar, A.[Aditya], Ravichandran, A.[Avinash], Polito, M.[Marzia], Soatto, S.[Stefano],
LQF: Linear Quadratic Fine-Tuning,
CVPR21(15724-15734)
IEEE DOI 2111
Deep learning, Training data, Computer architecture, Robustness, Pattern recognition, Task analysis BibRef

Sadhukhan, R., Saha, A., Mukhopadhyay, J., Patra, A.,
Knowledge Distillation Inspired Fine-Tuning Of Tucker Decomposed CNNS and Adversarial Robustness Analysis,
ICIP20(1876-1880)
IEEE DOI 2011
Robustness, Knowledge engineering, Convolution, Tensile stress, Neural networks, Perturbation methods, Acceleration, Adversarial Robustness BibRef

Li, Q.[Qi], Mai, L.[Long], Alcorn, M.A.[Michael A.], Nguyen, A.[Anh],
A Cost-effective Method for Improving and Re-purposing Large, Pre-trained GANs by Fine-tuning Their Class-embeddings,
ACCV20(IV:526-541).
Springer DOI 2103
BibRef

Tanveer, M.S.[Muhammad Suhaib], Khan, M.U.K.[Muhammad Umar Karim], Kyung, C.M.[Chong-Min],
Fine-Tuning DARTS for Image Classification,
ICPR21(4789-4796)
IEEE DOI 2105
Microprocessors, Image classification BibRef

Protopapadakis, E.[Eftychios], Doulamis, A.[Anastasios], Doulamis, N.[Nikolaos], Maltezos, E.[Evangelos],
Semi-supervised Fine-tuning for Deep Learning Models in Remote Sensing Applications,
ISVC20(I:719-730).
Springer DOI 2103
BibRef

Guo, Y.H.[Yun-Hui], Shi, H.H.[Hong-Hui], Kumar, A.[Abhishek], Grauman, K.[Kristen], Rosing, T.[Tajana], Feris, R.S.[Rogerio S.],
SpotTune: Transfer Learning Through Adaptive Fine-Tuning,
CVPR19(4800-4809).
IEEE DOI 2002
BibRef

Gui, L.Y., Gui, L., Wang, Y.X., Morency, L.P., Moura, J.M.F.,
Factorized Convolutional Networks: Unsupervised Fine-Tuning for Image Clustering,
WACV18(1205-1214)
IEEE DOI 1806
convolution, feedforward neural nets, image recognition, image representation, matrix decomposition, pattern clustering, Tuning BibRef

Ge, W., Yu, Y.,
Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-Tuning,
CVPR17(10-19)
IEEE DOI 1711
Kernel, Machine learning, Neural networks, Training data, Visualization BibRef

Chu, B.[Brian], Madhavan, V.[Vashisht], Beijbom, O.[Oscar], Hoffman, J.[Judy], Darrell, T.J.[Trevor J.],
Best Practices for Fine-Tuning Visual Classifiers to New Domains,
TASKCV16(III: 435-442).
Springer DOI 1611
Fine tuned generic to specific domain. BibRef

Rosa, G.[Gustavo], Papa, J.[João], Marana, A.[Aparecido], Scheirer, W.[Walter], Cox, D.[David],
Fine-Tuning Convolutional Neural Networks Using Harmony Search,
CIARP15(683-690).
Springer DOI 1511
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Constraint Based Matching .


Last update:Jun 17, 2024 at 21:38:11