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In order to quickly adapt to new data, few-shot learning aims at learning from few examples, often by using already acquired knowledge.
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Bengio, Y., Courville, A., and Vincent, P · 2013
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Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Multi-task deep networks for drug target prediction
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Siamese neural networks for one-shot image recognition
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Deep learning
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Learning to learn by gradient descent by gradient descent
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Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., March, M., and Lempitsky, V · 2016
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One-shot learning of scene locations via feature trajectory transfer
Kwitt, R., Hegenbart, S., and Niethammer, M · 2016
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Deeptox: toxicity prediction using deep learning
Mayr, A., Klambauer, G., Unterthiner, T., and Hochreiter, S · 2016
Cited alongside, same era.
Using deep learning for image-based plant disease detection
Mohanty, S. P., Hughes, D. P., and Salathé, M · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X., and Chen, X · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., and Wierstra, D · 2016
Cited alongside, same era.
Aga: Attribute-guided augmentation
Dixit, M., Kwitt, R., Niethammer, M., and Vasconcelos, N · 2017
Cited alongside, same era.
Deep triplet ranking networks for one-shot recognition
Ye, M. and Guo, Y · 2018
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Infinite mixture prototypes for few-shot learning
Allen, K., Shelhamer, E., Shin, H., and Tenenbaum, J · 2019
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Codella, N., Rotemberg, V., Tschandl, P., Celebi, M. E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., et al · 2019
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Diversity with cooperation: Ensemble methods for few-shot classification
Dvornik, N., Schmid, C., and Mairal, J · 2019
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A new benchmark for evaluation of cross-domain few-shot learning
Guo, Y., Codella, N., Karlinsky, L., Smith, J., Rosing, T., and Feris, R · 2019
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Cited alongside, same era.
The chembl database in 2017
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Cited alongside, same era.
Low-shot visual recognition by shrinking and hallucinating features
Hariharan, B. and Girshick, R · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Cited alongside, same era.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Cited alongside, same era.
Editorial: Tox21 challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental toxicants and drugs. front
Huang, R. and Xia, M · 2017
Cited alongside, same era.
Later among the works it cites.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Helber, P., Bischke, B., Dengel, A., and Borth, D · 2019
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Accurate prediction of biological assays with high-throughput microscopy images and convolutional networks
Hofmarcher, M., Rumetshofer, E., Clevert, D., Hochreiter, S., and Klambauer, G · 2019
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Cross attention network for few-shot classification
Hou, R., Chang, H., Bingpeng, M., Shan, S., and Chen, X · 2019
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A review of domain adaptation without target labels
Kouw, W. M. and Loog, M · 2019
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Meta-learning with differentiable convex optimization
Lee, K., Maji, S., Ravichandran, A., and Soatto, S · 2019
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Finding task-relevant features for few-shot learning by category traversal
Li, H., Eigen, D., Dodge, S., Zeiler, M., and Wang, X · 2019
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Learning to propagate labels: Transductive propagation network for few-shot learning
Liu, Y., Lee, J., Park, M., Kim, S., Yang, E., Hwang, S., and Yang, Y · 2019
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Low-shot learning from imaginary 3d model
Pahde, F., Puscas, M., Wolff, J., Klein, T., Sebe, N., and Nabi, M · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Meta-learning with implicit gradients
Rajeswaran, A., Finn, C., Kakade, S., and Levine, S · 2019
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Meta-transfer learning for few-shot learning
Sun, Q., Liu, Y., Chua, T., and Schiele, B · 2019
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d-sne: Domain adaptation using stochastic neighborhood embedding
Xu, X., Zhou, X., Venkatesan, R., Swaminathan, G., and Majumder, O · 2019
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Tapnet: Neural network augmented with task-adaptive projection for few-shot learning
Yoon, S., Seo, J., and Moon, J · 2019
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Variational few-shot learning
Zhang, J., Zhao, C., Ni, B., Xu, M., and Yang, X · 2019
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Sb-mtl: Score-based meta transfer-learning for cross-domain few-shot learning
Cai, J., Cai, B., and Shen, S. M · 2020
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How well do self-supervised models transfer?
Ericsson, L., Gouk, H., and Hospedales, T · 2020
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Learning robust representations via multi-view information bottleneck
Federici, M., Dutta, A., Forré, P., Kushman, N., and Akata, Z · 2020
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Meta-learning in neural networks: A survey
Hospedales, T., Antoniou, A., Micaelli, P., and Storkey, A · 2020
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On episodes, prototypical networks, and few-shot learning
Laenen, S. and Bertinetto, L · 2020
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Learning from very few samples: A survey, 2020
Lu, J., Gong, P., Ye, J., and Zhang, C · 2020
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Hopfield networks is all you need
Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Adler, T., Gruber, L., Holzleitner, M., Pavlović, M., Sandve, G., Greiff, V., Kreil, D., Kopp, M., Klambauer, G., Brandstetter, J., and Hochreiter, S · 2020
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Industry-scale application and evaluation of deep learning for drug target prediction
Sturm, N., Mayr, A., Van, T. L., Chupakhin, V., Ceulemans, H., Wegner, J., Golib-Dzib, J., Jeliazkova, N., Vandriessche, Y., Böhm, S., et al · 2020
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On mutual information maximization for representation learning
Tschannen, M., Djolonga, J., Rubenstein, P. K., Gelly, S., and Lucic, M · 2020
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Cross-domain few-shot classification via learned feature-wise transformation
Tseng, H., Lee, H., Huang, J., and Yang, M · 2020
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A comparison of machine learning methods for cross-domain few-shot learning
Wang, H., Gouk, H., Frank, E., Pfahringer, B., and Mayo, M · 2020
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Few-shot learning via embedding adaptation with set-to-set functions
Ye, H., Hu, H., Zhan, D., and Sha, F · 2020
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Shallow bayesian meta learning for real-world few-shot recognition
Xueting, Z., Meng, D., Gouk, H., and Hospedales, T · 2021
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