Fetching the paper…
Reading the bibliography…
Conventional image classifiers are trained by randomly sampling mini-batches of images.
A closer look at few-shot classification
Chen, W.-Y., Liu, Y.-C., Kira, Z., Wang, Y.-C. F., and Huang, J.-B · 1904
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
Earlier work this paper cites.
Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
Earlier work this paper cites.
mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2017
Earlier work this paper cites.
Meta-learning with differentiable closed-form solvers
Bertinetto, L., Henriques, J. F., Torr, P. H., and Vedaldi, A · 2018
Earlier work this paper cites.
Autoaugment: Learning augmentation policies from data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V · 2018
Earlier work this paper cites.
Dynamic few-shot visual learning without forgetting
Gidaris, S. and Komodakis, N · 2018
Cited alongside, same era.
On first-order meta-learning algorithms
Nichol, A., Achiam, J., and Schulman, J · 2018
Cited alongside, same era.
Tadam: Task dependent adaptive metric for improved few-shot learning
Oreshkin, B., López, P. R., and Lacoste, A · 2018
Cited alongside, same era.
Few-shot image recognition by predicting parameters from activations
Qiao, S., Liu, C., Shen, W., and Yuille, A. L · 2018
Cited alongside, same era.
Antoniou, A. and Storkey, A · 2019
Cited alongside, same era.
Meta-learning without memorization
Yin, M., Tucker, G., Zhou, M., Levine, S., and Finn, C · 2019
Later among the works it cites.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., and Yoo, Y · 2019
Later among the works it cites.
Unraveling meta-learning: Understanding feature representations for few-shot tasks
Goldblum, M., Reich, S., Fowl, L., Ni, R., Cherepanova, V., and Goldstein, T · 2020
Closest in time.
Maxup: A simple way to improve generalization of neural network training
Gong, C., Ren, T., Ye, M., and Liu, Q · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Goldblum, M., Fowl, L., and Goldstein, T · 2019
Cited alongside, same era.
A closer look at feature space data augmentation for few-shot intent classification
Kumar, V., Glaude, H., de Lichy, C., and Campbell, W · 2019
Cited alongside, same era.
Meta-learning with differentiable convex optimization
Lee, K., Maji, S., Ravichandran, A., and Soatto, S · 2019
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks, 2019
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2019
Cited alongside, same era.
Meta-dataset: A dataset of datasets for learning to learn from few examples
Triantafillou, E., Zhu, T., Dumoulin, V., Lamblin, P., Evci, U., Xu, K., Goroshin, R., Gelada, C., Swersky, K., Manzagol, P.-A., et al · 2019
Cited alongside, same era.
Image block augmentation for one-shot learning
Chen, Z., Fu, Y., Chen, K., and Jiang, Y.-G
Cited in the paper.
Image deformation meta-networks for one-shot learning
Chen, Z., Fu, Y., Wang, Y.-X., Ma, L., Liu, W., and Hebert, M
Cited in the paper.
Kye, S. M., Lee, H. B., Kim, H., and Hwang, S. J · 2020
Closest in time.
Task augmentation by rotating for meta-learning
Liu, J., Chao, F., and Lin, C.-M · 2020
Closest in time.
Meta-learning requires meta-augmentation
Rajendran, J., Irpan, A., and Jang, E · 2020
Closest in time.
Self-augmentation: Generalizing deep networks to unseen classes for few-shot learning
Seo, J.-W., Jung, H.-G., and Lee, S.-W · 2020
Closest in time.
Don’t overlook the support set: Towards improving generalization in meta-learning
Yao, H., Huang, L., Wei, Y., Tian, L., Huang, J., and Li, Z · 2020
Closest in time.