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Few-shot language learners adapt knowledge from a pre-trained model to recognize novel classes from a few-labeled sentences.
Fine-tuning pretrained language models: Weight initializations, data orders, and early stopping
Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, and Noah A. Smith. 2020 · 2002
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017 · 2017
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Low-shot visual recognition by shrinking and hallucinating features
Bharath Hariharan and Ross Girshick. 2017 · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. 2017 · 2017
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Low-shot learning from imaginary data
Yu-Xiong Wang, Ross Girshick, Martial Hebert, and Bharath Hariharan. 2018 · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
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A closer look at feature space data augmentation for few-shot intent classification
Varun Kumar, Hadrien Glaude, Cyprien de Lichy, and Wlliam Campbell. 2019 · 2019
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Mixout: Effective regularization to finetune large-scale pretrained language models
Cheolhyoung Lee, Kyunghyun Cho, and Wanmo Kang. 2019 · 2019
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019 · 2019
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EDA: Easy data augmentation techniques for boosting performance on text classification tasks
Jason Wei and Kai Zou. 2019 · 2019
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Self-supervised meta-learning for few-shot natural language classification tasks
Trapit Bansal, Rishikesh Jha, Tsendsuren Munkhdalai, and Andrew McCallum. 2020 · 2020
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Few-shot complex knowledge base question answering via meta reinforcement learning
Yuncheng Hua, Yuan-Fang Li, Gholamreza Haffari, Guilin Qi, and Tongtong Wu. 2020 · 2020
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Task meta-transfer from limited parallel labels
Yiren Jian, Karim Ahmed, and Lorenzo Torresani. 2020 · 2020
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SMART: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization
Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Tuo Zhao. 2020 · 2020
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Adversarial feature hallucination networks for few-shot learning
Kai Li, Yulun Zhang, Kunpeng Li, and Yun Fu. 2020 · 2020
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Meta-learning for few-shot nmt adaptation
Amr Sharaf, Hany Hassan, and Hal Daumé III. 2020 · 2020
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Rethinking few-shot image classification: a good embedding is all you need?
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B Tenenbaum, and Phillip Isola. 2020 · 2020
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Enhanced generative adversarial network for 3d brain mri super-resolution
Jiancong Wang, Yuhua Chen, Yifan Wu, Jianbo Shi, and James Gee. 2020 · 2020
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Freelb: Enhanced adversarial training for natural language understanding
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, and Jingjing Liu. 2020 · 2020
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Few-shot learning through contextual data augmentation
Farid Arthaud, Rachel Bawden, and Alexandra Birch. 2021 · 2021
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Semi-supervised meta-learning for cross-domain few-shot intent classification
Judith Yue Li and Jiong Zhang. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Towards understanding and mitigating social biases in language models
Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, and Ruslan Salakhutdinov. 2021 · 2021
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Few-shot learning via feature hallucination with variational inference
Qinxuan Luo, Lingfeng Wang, Jingguo Lv, Shiming Xiang, and Chunhong Pan. 2021 · 2021
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Few-shot question answering by pretraining span selection
Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, and Omer Levy. 2021 · 2021
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Cited alongside, same era.
Semi-supervised few-shot intent classification and slot filling
Samyadeep Basu, Karine lp Kiun Chong, Amr Sharaf, Alex Fischer, Vishal Rohra, Michael Amoake, Hazem El-Hammamy, Ehi Nosakhare, Vijay Ramani, and Benjamin Han. 2021 · 2021
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Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning
Paola Cascante-Bonilla, Fuwen Tan, Yanjun Qi, and Vicente Ordonez. 2021 · 2021
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Meta-learning for few-shot named entity recognition
Cyprien de Lichy, Alexa AI Amazon, Hadrien Glaude, and William Campbell. 2021 · 2021
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Few-nerd: A few-shot named entity recognition dataset
Ning Ding, Guangwei Xu, Yulin Chen, Xiaobin Wang, Xu Han, Pengjun Xie, Hai-Tao Zheng, and Zhiyuan Liu. 2021 · 2021
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Improving zero and few-shot abstractive summarization with intermediate fine-tuning and data augmentation
Alexander Fabbri, Simeng Han, Haoyuan Li, Haoran Li, Marjan Ghazvininejad, Shafiq Joty, Dragomir Radev, and Yashar Mehdad. 2021 · 2021
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
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Exploiting cloze-questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2021 · 2021
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Improving and simplifying pattern exploiting training
Derek Tam, Rakesh R Menon, Mohit Bansal, Shashank Srivastava, and Colin Raffel. 2021 · 2021
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Learning from miscellaneous other-class words for few-shot named entity recognition
Meihan Tong, Shuai Wang, Bin Xu, Yixin Cao, Minghui Liu, Lei Hou, and Juanzi Li. 2021 · 2021
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Few-shot text classification with triplet networks, data augmentation, and curriculum learning
Jason Wei, Chengyu Huang, Soroush Vosoughi, Yu Cheng, and Shiqi Xu. 2021 · 2021
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Revisiting few-sample {bert} fine-tuning
Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q Weinberger, and Yoav Artzi. 2021 · 2021
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Contrastive learning for prompt-based few-shot language learners
Yiren Jian, Chongyang Gao, and Soroush Vosoughi. 2022 · 2022
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Label hallucination for few-shot classification
Yiren Jian and Lorenzo Torresani. 2022 · 2022
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Tensor feature hallucination for few-shot learning
Michalis Lazarou, Tania Stathaki, and Yannis Avrithis. 2022 · 2022
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Adversarial semantic hallucination for domain generalized semantic segmentation
Gabriel Tjio, Ping Liu, Joey Tianyi Zhou, and Rick Siow Mong Goh. 2022 · 2022
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