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Human image generation is a key focus in image synthesis due to its broad applications, but even slight inaccuracies in anatomy, pose, or details can compromise realism.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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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 · 2011
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Self-paced dictionary learning for image classification
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Auto-encoding variational bayes
Diederik P Kingma · 2013
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Generative adversarial nets
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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Cassl: Curriculum accelerated self-supervised learning
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Arcface: Additive angular margin loss for deep face recognition
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Curriculum-guided hindsight experience replay
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A style-based generator architecture for generative adversarial networks
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Plato-2: Towards building an open-domain chatbot via curriculum learning
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Denoising diffusion probabilistic models
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Norm-based curriculum learning for neural machine translation
Xuebo Liu, Houtim Lai, Derek F Wong, and Lidia S Chao · 2020
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Accelerating reinforcement learning for reaching using continuous curriculum learning
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The balanced loss curriculum learning
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Image difficulty curriculum for generative adversarial networks (cugan)
Petru Soviany, Claudiu Ardei, Radu Tudor Ionescu, and Marius Leordeanu · 2020
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Reinforced curriculum learning on pre-trained neural machine translation models
Mingjun Zhao, Haijiang Wu, Di Niu, and Xiaoli Wang · 2020
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Uncertainty-aware curriculum learning for neural machine translation
Yikai Zhou, Baosong Yang, Derek F Wong, Yu Wan, and Lidia S Chao · 2020
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Curriculum learning for face recognition
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8-bit optimizers via block-wise quantization
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Diffusion models beat gans on image synthesis
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Alias-free generative adversarial networks
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Sdxl: Improving latent diffusion models for high-resolution image synthesis
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
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Instantbooth: Personalized text-to-image generation without test-time finetuning
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Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon · 2021
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Learning transferable visual models from natural language supervision
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Meta-curriculum learning for domain adaptation in neural machine translation
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Using human feedback to fine-tune diffusion models without any reward model
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Self-play fine-tuning converts weak language models to strong language models
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