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With the development of AI-Generated Content (AIGC), text-to-audio models are gaining widespread attention.
Adam: A method for stochastic optimization
DiederikP. Kingma and Jimmy Ba · 2014
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba · 2015
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Reliability and learnability of human bandit feedback for sequence-to-sequence reinforcement learning
Julia Kreutzer, Joshua Uyheng, and Stefan Riezler · 2018
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Reinforcement learning and control as probabilistic inference: Tutorial and review
Sergey Levine · 2018
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Learning to extract coherent summary via deep reinforcement learning
Yuxiang Wu and Baotian Hu · 2018
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Fréchet audio distance: A reference-free metric for evaluating music enhancement algorithms
Kevin Kilgour, Mauricio Zuluaga, Dominik Roblek, and Matthew Sharifi · 2019
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Audiocaps: Generating captions for audios in the wild
Chris Dongjoo Kim, Byeongchang Kim, Hyunmin Lee, and Gunhee Kim · 2019
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Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis
Jungil Kong, Jaehyeon Kim, and Jaekyoung Bae · 2020
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Panns: Large-scale pretrained audio neural networks for audio pattern recognition
Qiuqiang Kong, Yin Cao, Turab Iqbal, Yuxuan Wang, Wenwu Wang, and Mark D. Plumbley · 2020
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Learning to summarize from human feedback
Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, and Paul Christiano · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alex Nichol · 2021
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Audiogen: Textually guided audio generation
Felix Kreuk, Gabriel Synnaeve, Adam Polyak, Uriel Singer, Alexandre Défossez, Jade Copet, Devi Parikh, Yaniv Taigman, and Yossi Adi · 2022
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Training language models to follow instructions with human feedback, 2022
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Aligning text-to-image models using human feedback, 2023
Kimin Lee, Hao Liu, Moonkyung Ryu, Olivia Watkins, Yuqing Du, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, and Shixiang Shane Gu · 2023
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AudioLDM: Text-to-audio generation with latent diffusion models
Haohe Liu, Zehua Chen, Yi Yuan, Xinhao Mei, Xubo Liu, Danilo Mandic, Wenwu Wang, and Mark D Plumbley · 2023
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AudioLDM 2: Learning holistic audio generation with self-supervised pretraining
Haohe Liu, Qiao Tian, Yi Yuan, Xubo Liu, Xinhao Mei, Qiuqiang Kong, Yuping Wang, Wenwu Wang, Yuxuan Wang, and Mark D. Plumbley · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Mixture-of-experts meets instruction tuning:a winning combination for large language models, 2023
Sheng Shen, Le Hou, Yanqi Zhou, Nan Du, Shayne Longpre, Jason Wei, Hyung Won Chung, Barret Zoph, William Fedus, Xinyun Chen, Tu Vu, Yuexin Wu, Wuyang Chen, Albert Webson, Yunxuan Li, Vincent Zhao, Hongkun Yu, Kurt Keutzer, Trevor Darrell, and Denny Zhou · 2023
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Introducing claude, 2023
Anthropic · 2023
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Open problems and fundamental limitations of reinforcement learning from human feedback
Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, et al · 2023
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Raft: Reward ranked finetuning for generative foundation model alignment, 2023
Hanze Dong, Wei Xiong, Deepanshu Goyal, Yihan Zhang, Winnie Chow, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, and Tong Zhang · 2023
Cited alongside, same era.
Text-to-audio generation using instruction tuned llm and latent diffusion model
Deepanway Ghosal, Navonil Majumder, Ambuj Mehrish, and Soujanya Poria · 2023
Cited alongside, same era.
Bard, 2023
Google · 2023
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Audio generation with multiple conditional diffusion model, 2023
Zhifang Guo, Jianguo Mao, Rui Tao, Long Yan, Kazushige Ouchi, Hong Liu, and Xiangdong Wang · 2023
Cited alongside, same era.
Make-an-audio 2: Temporal-enhanced text-to-audio generation, 2023
Jiawei Huang, Yi Ren, Rongjie Huang, Dongchao Yang, Zhenhui Ye, Chen Zhang, Jinglin Liu, Xiang Yin, Zejun Ma, and Zhou Zhao · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation
Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, and Shlomo Dubnov · 2023
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Diffsound: Discrete diffusion model for text-to-sound generation
Dongchao Yang, Jianwei Yu, Helin Wang, Wen Wang, Chao Weng, Yuexian Zou, and Dong Yu · 2023
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Using human feedback to fine-tune diffusion models without any reward model
Kai Yang, Jian Tao, Jiafei Lyu, Chunjiang Ge, Jiaxin Chen, Qimai Li, Weihan Shen, Xiaolong Zhu, and Xiu Li · 2023
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Gpt4tools: Teaching large language model to use tools via self-instruction
Rui Yang, Lin Song, Yanwei Li, Sijie Zhao, Yixiao Ge, Xiu Li, and Ying Shan · 2023
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Training diffusion models with reinforcement learning, 2024
Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine · 2024
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Auffusion: Leveraging the power of diffusion and large language models for text-to-audio generation, 2024
Jinlong Xue, Yayue Deng, Yingming Gao, and Ya Li · 2024
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Retrieval-augmented text-to-audio generation, 2024
Yi Yuan, Haohe Liu, Xubo Liu, Qiushi Huang, Mark D. Plumbley, and Wenwu Wang · 2024
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