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In this work we propose a HyperTransformer, a Transformer-based model for supervised and semi-supervised few-shot learning that generates weights of a convolutional neural network (CNN) directly from support samples.
Siamese neural networks for one-shot image recognition
Koch, G., Zemel, R., Salakhutdinov, R., et al · 2015
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Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., and Wierstra, D · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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HyperNetworks
Ha, D., Dai, A. M., and Le, Q. V · 2017
Earlier work this paper cites.
Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R. S · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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Dynamic few-shot visual learning without forgetting
Gidaris, S. and Komodakis, N · 2018
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Umap: Uniform manifold approximation and projection for dimension reduction
McInnes, L., Healy, J., and Melville, J · 2018
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On first-order meta-learning algorithms
Nichol, A., Achiam, J., and Schulman, J · 2018
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TADAM: task dependent adaptive metric for improved few-shot learning
Oreshkin, B. N., López, P. R., and Lacoste, A · 2018
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Low-shot learning with imprinted weights
Qi, H., Brown, M., and Lowe, D. G · 2018
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Few-shot image recognition by predicting parameters from activations
Qiao, S., Liu, C., Shen, W., and Yuille, A. L · 2018
Earlier work this paper cites.
Meta-learning for semi-supervised few-shot classification
Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., and Zemel, R. S · 2018
Earlier work this paper cites.
Learning to compare: Relation network for few-shot learning
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H. S., and Hospedales, T. M · 2018
Earlier work this paper cites.
Infinite mixture prototypes for few-shot learning
Allen, K. R., Shelhamer, E., Shin, H., and Tenenbaum, J. B · 2019
Cited alongside, same era.
How to train your MAML
Antoniou, A., Edwards, H., and Storkey, A. J · 2019
Cited alongside, same era.
Collect and select: Semantic alignment metric learning for few-shot learning
Hao, F., He, F., Cheng, J., Wang, L., Cao, J., and Tao, D · 2019
Cited alongside, same era.
Task agnostic meta-learning for few-shot learning
Jamal, M. A. and Qi, G · 2019
Cited alongside, same era.
Few-shot learning with global class representations
Li, A., Luo, T., Xiang, T., Huang, W., and Wang, L · 2019
Cited alongside, same era.
Lgm-net: Learning to generate matching networks for few-shot learning
Li, H., Dong, W., Mei, X., Ma, C., Huang, F., and Hu, B · 2019
Cited alongside, same era.
Rethinking few-shot image classification: A good embedding is all you need?
Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J. B., and Isola, P · 2020
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Learning texture transformer network for image super-resolution
Yang, F., Yang, H., Fu, J., Lu, H., and Guo, B · 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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Meta-learning via hypernetworks
Zhao, D., von Oswald, J., Kobayashi, S., Sacramento, J., and Grewe, B. F · 2020
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Pre-trained image processing transformer
Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., and Gao, W · 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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Learning to propagate labels: Transductive propagation network for few-shot learning
Liu, Y., Lee, J., Park, M., Kim, S., Yang, E., Hwang, S. J., and Yang, Y · 2019
Cited alongside, same era.
Few-shot image recognition with knowledge transfer
Peng, Z., Li, Z., Zhang, J., Li, Y., Qi, G., and Tang, J · 2019
Cited alongside, same era.
Hypergan: A generative model for diverse, performant neural networks
Ratzlaff, N. and Li, F · 2019
Cited alongside, same era.
Fast and flexible multi-task classification using conditional neural adaptive processes
Requeima, J., Gordon, J., Bronskill, J., Nowozin, S., and Turner, R. E · 2019
Cited alongside, same era.
Meta-learning with latent embedding optimization
Rusu, A. A., Rao, D., Sygnowski, J., Vinyals, O., Pascanu, R., Osindero, S., and Hadsell, R · 2019
Cited alongside, same era.
PARN: position-aware relation networks for few-shot learning
Wu, Z., Li, Y., Guo, L., and Jia, K · 2019
Cited alongside, same era.
Later among the works it cites.
MELR: meta-learning via modeling episode-level relationships for few-shot learning
Fei, N., Lu, Z., Xiang, T., and Huang, S · 2021
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A universal representation transformer layer for few-shot image classification
Liu, L., Hamilton, W. L., Long, G., Jiang, J., and Larochelle, H · 2021
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Parameter-efficient multi-task fine-tuning for transformers via shared hypernetworks
Mahabadi, R. K., Ruder, S., Dehghani, M., and Henderson, J · 2021
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Conditionally adaptive multi-task learning: Improving transfer learning in NLP using fewer parameters & less data
Pilault, J., Elhattami, A., and Pal, C. J · 2021
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Hypergrid transformers: Towards A single model for multiple tasks
Tay, Y., Zhao, Z., Bahri, D., Metzler, D., and Juan, D · 2021
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Training data-efficient image transformers & distillation through attention
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., and Jégou, H · 2021
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Learning to generate task-specific adapters from task description
Ye, Q. and Ren, X · 2021
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Deformable DETR: deformable transformers for end-to-end object detection
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J · 2021
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