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Self-supervised learning (SSL) is a powerful technique for learning representations from unlabeled data.
“Learning multiple visual domains with residual adapters,”
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi, · 2017
Earlier work this paper cites.
“Efficient parametrization of multi-domain deep neural networks,”
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi, · 2018
Earlier work this paper cites.
“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
Earlier work this paper cites.
“An unsupervised autoregressive model for speech representation learning,”
Yu-An Chung, Wei-Ning Hsu, Hao Tang, and James Glass, · 2019
Earlier work this paper cites.
“wav2vec 2.0: A framework for self-supervised learning of speech representations,”
Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli, · 2020
Earlier work this paper cites.
“Adapterfusion: Non-destructive task composition for transfer learning,”
Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, Kyunghyun Cho, and Iryna Gurevych, · 2020
Earlier work this paper cites.
“Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models,”
Elad Ben Zaken, Shauli Ravfogel, and Yoav Goldberg, · 2021
Cited alongside, same era.
“Superb: Speech processing universal performance benchmark,”
Shu-wen Yang, Po-Han Chi, Yung-Sung Chuang, Cheng-I Jeff Lai, Kushal Lakhotia, Yist Y Lin, Andy T Liu, Jiatong Shi, Xuankai Chang, Guan-Ting Lin, et al., · 2021
Cited alongside, same era.
“Exploiting adapters for cross-lingual low-resource speech recognition,”
Wenxin Hou, Han Zhu, Yidong Wang, Jindong Wang, Tao Qin, Renjun Xu, and Takahiro Shinozaki, · 2021
Cited alongside, same era.
“Voice2series: Reprogramming acoustic models for time series classification,”
Chao-Han Huck Yang, Yun-Yun Tsai, and Pin-Yu Chen, · 2021
Cited alongside, same era.
“Hubert: Self-supervised speech representation learning by masked prediction of hidden units,”
Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, and Abdelrahman Mohamed, · 2021
“AdapterBias: Parameter-efficient token-dependent representation shift for adapters in NLP tasks,”
Chin-Lun Fu, Zih-Ching Chen, Yun-Ru Lee, and Hung-yi Lee, · 2022
Closest in time.
“Self-supervised speech representation learning: A review,” 2022
Abdelrahman Mohamed, Hung-yi Lee, Lasse Borgholt, Jakob D. Havtorn, Joakim Edin, Christian Igel, Katrin Kirchhoff, Shang-Wen Li, Karen Livescu, Lars Maaløe, Tara N. Sainath, and Shinji Watanabe, · 2022
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“Efficient adapter transfer of self-supervised speech models for automatic speech recognition,”
Bethan Thomas, Samuel Kessler, and Salah Karout, · 2022
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“An adapter based pre-training for efficient and scalable self-supervised speech representation learning,”
Samuel Kessler, Bethan Thomas, and Salah Karout, · 2022
Closest in time.
“Exploring efficient-tuning methods in self-supervised speech models,”
Zih-Ching Chen, Chin-Lun Fu, Chih-Ying Liu, Shang-Wen Li, and Hung-yi Lee, · 2022
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Cited alongside, same era.
“On the effectiveness of adapter-based tuning for pretrained language model adaptation,”
Ruidan He, Linlin Liu, Hai Ye, Qingyu Tan, Bosheng Ding, Liying Cheng, Jia-Wei Low, Lidong Bing, and Luo Si, · 2021
Cited alongside, same era.
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“Towards a unified view of parameter-efficient transfer learning,”
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig, · 2022
Closest in time.