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Transfer-learning methods aim to improve performance in a data-scarce target domain using a model pretrained on a data-rich source domain.
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Efficient and robust feature selection via joint ℓ 2 , 1 \ell_{2,1} -norms minimization
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Sun database: Large-scale scene recognition from abbey to zoo
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Efficient spectral feature selection with minimum redundancy
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Cats and dogs
Parkhi, O. M., Vedaldi, A., Zisserman, A., and Jawahar, C. V · 2012
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Vision meets robotics: The kitti dataset
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Describing textures in the wild
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Feature Selection for High-Dimensional Data
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Kaggle diabetic retinopathy detection, July 2015
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Imagenet large scale visual recognition challenge
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Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
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Branchynet: Fast inference via early exiting from deep neural networks
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Cross-domain few-shot learning by representation fusion
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A survey on transfer learning in natural language processing
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Analyzing redundancy in pretrained transformer models
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Selecting relevant features from a multi-domain representation for few-shot classification
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Transfer learning for music classification and regression tasks
Choi, K., Fazekas, G., Sandler, M. B., and Cho, K · 2017
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Feature selection based on structured sparsity: A comprehensive study
Gui, J., Sun, Z., Ji, S., Tao, D., and Tan, T · 2017
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
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Multi-level and multi-scale feature aggregation using pretrained convolutional neural networks for music auto-tagging
Lee, J. and Nam, J · 2017
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Feature pyramid networks for object detection
Lin, T.-Y., Dollár, P., Girshick, R. B., He, K., Hariharan, B., and Belongie, S. J · 2017
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dsprites: Disentanglement testing sprites dataset
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Learning multiple visual domains with residual adapters
Rebuffi, S.-A., Bilen, H., and Vedaldi, A · 2017
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Dvornik, N., Schmid, C., and Mairal, J · 2020
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A broader study of cross-domain few-shot learning
Guo, Y., Codella, N. C., Karlinsky, L., Codella, J. V., Smith, J. R., Saenko, K., Rosing, T., and Feris, R · 2020
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Gradients as features for deep representation learning
Mu, F., Liang, Y., and Li, Y · 2020
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Deep ensembles for low-data transfer learning
Mustafa, B., Riquelme, C., Puigcerver, J., andAndr’e Susano Pinto, Keysers, D., and Houlsby, N · 2020
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Towards learning a universal non-semantic representation of speech
Shor, J., Jansen, A., Maor, R., Lang, O., Tuval, O., de Chaumont Quitry, F., Tagliasacchi, M., Shavitt, I., Emanuel, D., and Haviv, Y. A · 2020
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Deep transfer learning with ridge regression
Tang, S. and de Sa, V. R · 2020
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Bert loses patience: Fast and robust inference with early exit
Zhou, W., Xu, C., Ge, T., McAuley, J., Xu, K., and Wei, F · 2020
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Transfer learning in deep reinforcement learning: A survey
Zhu, Z., Lin, K., and Zhou, J · 2020
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A comprehensive survey on transfer learning
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., and He, Q · 2020
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Deep learning through the lens of example difficulty
Baldock, R., Maennel, H., and Neyshabur, B · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
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Comparing transfer and meta learning approaches on a unified few-shot classification benchmark
Dumoulin, V., Houlsby, N., Evci, U., Zhai, X., Goroshin, R., Gelly, S., and Larochelle, H · 2021
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Learn-to-share: A hardware-friendly transfer learning framework exploiting computation and parameter sharing
Fu, C., Huang, H., Chen, X., Tian, Y., and Zhao, J · 2021
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Parameter-efficient transfer learning with diff pruning
Guo, D., Rush, A. M., and Kim, Y · 2021
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A universal representation transformer layer for few-shot image classification
Liu, L., Hamilton, W., Long, G., Jiang, J., and Larochelle, H · 2021
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No one representation to rule them all: Overlapping features of training methods
Lopes, R. G., Dauphin, Y., and Cubuk, E. D · 2021
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Fast adaptation with linearized neural networks
Maddox, W., Tang, S., Moreno, P. G., Wilson, A. G., and Damianou, A. C · 2021
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Scalable transfer learning with expert models
Puigcerver, J., Riquelme, C., Mustafa, B., Renggli, C., Pinto, A. S., Gelly, S., Keysers, D., and Houlsby, N · 2021
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Projected gans converge faster
Sauer, A., Chitta, K., Müller, J., and Geiger, A · 2021
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Universal paralinguistic speech representations using self-supervised conformers
Shor, J., Jansen, A., Han, W., Park, D., and Zhang, Y · 2021
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Learning a universal template for few-shot dataset generalization
Triantafillou, E., Larochelle, H., Zemel, R., and Dumoulin, V · 2021
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Improving the post-hoc calibration of modern neural networks with probe scaling, 2022
Khalifa, A. and Alabdulmohsin, I · 2022
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