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The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors.
Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel · 1965
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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Continual learning with deep generative replay
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Compositional Visual Generation with Energy Based Models
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Denoising diffusion probabilistic models
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Zhan Shi, Xu Zhou, Xipeng Qiu, and Xiaodan Zhu · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Unsupervised learning of compositional energy concepts
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CLIPScore: A reference-free evaluation metric for image captioning
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Classifier-Free Diffusion Guidance
Jonathan Ho and Tim Salimans · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Maximum Likelihood Training of Score-Based Diffusion Models
Yang Song, Conor Durkan, Iain Murray, and Stefano Ermon · 2021
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Learning to prompt for continual learning
Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, and Tomas Pfister · 2022
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Differentially private diffusion models generate useful synthetic images
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Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models
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