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In computer vision, fine-tuning is the de-facto approach to leverage pre-trained vision models to perform downstream tasks.
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
F. Li, Rob Fergus, and Pietro Perona · 2004
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Automated flower classification over a large number of classes
M.E. Nilsback and A. Zisserman · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and F. Li · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Novel dataset for fine-grained image categorization: Stanford dogs
A. Khosla, N. Jayadevaprakash, B. Yao, and F. Li · 2011
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Cats and dogs
O.M. Parkhi, A. Vedaldi, A. Zisserman, and CV Jawahar · 2012
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Analyzing the performance of multilayer neural networks for object recognition
P. Agrawal, R. Girshick, and J. Malik · 2014
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Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Show and tell: A neural image caption generator
O. Vinyals, A. Toshev, S. Bengio, and D. Erhan · 2015
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Learning from massive noisy labeled data for image classification
T. Xiao, T. Xia, Y. Yang, C. Huang, and X. Wang · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Xception: Deep learning with depthwise separable convolutions
F. Chollet · 2017
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Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
G. Lin, A. Milan, C. Shen, and I. Reid · 2017
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Feature pyramid networks for object detection
T. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
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Learning multiple visual domains with residual adapters
S.A. Rebuffi, H. Bilen, and A. Vedaldi · 2017
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Scene parsing through ade20k dataset
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba · 2017
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Bottom-up and top-down attention for image captioning and visual question answering
P. Anderson, X. He, C. Buehler, D. Teney, M. Johnson, S. Gould, and L. Zhang · 2018
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
Cited alongside, same era.
Improving language understanding by generative pre-training
A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever · 2018
Cited alongside, same era.
Unified perceptual parsing for scene understanding
T. Xiao, Y. Liu, B. Zhou, Y. Jiang, and J. Sun · 2018
Cited alongside, same era.
Explicit inductive bias for transfer learning with convolutional networks
L. Xuhong, Y. Grandvalet, and F. Davoine · 2018
Cited alongside, same era.
Cascade r-cnn: high quality object detection and instance segmentation
Z. Cai and N. Vasconcelos · 2019
Cited alongside, same era.
Learning imbalanced datasets with label-distribution-aware margin loss
K. Cao, C. Wei, A. Gaidon, N. Arechiga, and T. Ma · 2019
Cited alongside, same era.
olmpics-on what language model pre-training captures
Alon Talmor, Yanai Elazar, Yoav Goldberg, and Jonathan Berant · 2020
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Co-tuning for transfer learning
K. You, Z. Kou, M. Long, and J. Wang · 2020
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What makes instance discrimination good for transfer learning?
N. Zhao, Z. Wu, R.WH. Lau, and S. Lin · 2020
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Clip-adapter: Better vision-language models with feature adapters
P. Gao, S. Geng, R. Zhang, T. Ma, R. Fang, Y. Zhang, H. Li, and Y. Qiao · 2021
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Making pre-trained language models better few-shot learners
T. Gao, A. Fisch, and D. Chen · 2021
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Hybrid task cascade for instance segmentation
K. Chen, J. Pang, J. Wang, Y. Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Shi, W. Ouyang, et al · 2019
Cited alongside, same era.
Catastrophic forgetting meets negative transfer: Batch spectral shrinkage for safe transfer learning
X. Chen, S. Wang, B. Fu, M. Long, and J. Wang · 2019
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
D. Hendrycks and T. Dietterich · 2019
Cited alongside, same era.
Parameter-efficient transfer learning for nlp
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, De L.Q., A. Gesmundo, M. Attariyan, and S. Gelly · 2019
Cited alongside, same era.
Do better imagenet models transfer better?
S. Kornblith, J. Shlens, and Q.V. Le · 2019
Cited alongside, same era.
Delta: Deep learning transfer using feature map with attention for convolutional networks
X. Li, H. Xiong, H. Wang, Y. Rao, L. Liu, Z. Chen, and J. Huan · 2019
Cited alongside, same era.
D. Hendrycks, S. Basart, N. Mu, S. Kadavath, F. Wang, E. Dorundo, R. Desai, T. Zhu, S. Parajuli, M. Guo, et al · 2021
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Natural adversarial examples
D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song · 2021
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A broad study on the transferability of visual representations with contrastive learning
A. Islam, C.Richard. Chen, R. Panda, L. Karlinsky, R. Radke, and R. Feris · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
C. Jia, Y. Yang, Y. Xia, Y. Chen, Z. Parekh, H. Pham, Q.V. Le, Y. Sung, Z. Li, and T. Duerig · 2021
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Mdetr-modulated detection for end-to-end multi-modal understanding
A. Kamath, M. Singh, Y. LeCun, G. Synnaeve, I. Misra, and N. Carion · 2021
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The power of scale for parameter-efficient prompt tuning
B. Lester, R. Al-Rfou, and N. Constant · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
X.L. Li and P. Liang · 2021
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P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo · 2021
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Learning transferable visual models from natural language supervision
A. Radford, J.W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al · 2021
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Training data-efficient image transformers & distillation through attention
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou · 2021
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Multimodal few-shot learning with frozen language models
M. Tsimpoukelli, J. Menick, S. Cabi, S. Eslami, O. Vinyals, and F. Hill · 2021
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Logme: Practical assessment of pre-trained models for transfer learning
K. You, Y. Liu, J. Wang, and M. Long · 2021
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Learning to prompt for vision-language models
K. Zhou, J. Yang, C.C. Loy, and Z. Liu · 2021
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