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Representation learning is all about discovering the hidden modular attributes that generate the data faithfully.
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beta-vae: Learning basic visual concepts with a constrained variational framework
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Ricky TQ Chen, Xuechen Li, Roger B Grosse, and David K Duvenaud · 2018
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Generative adversarial networks: An overview
Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, and Anil A Bharath · 2018
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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 · 2021
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Self-supervised learning disentangled group representation as feature
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Semantic hierarchy emerges in deep generative representations for scene synthesis
Ceyuan Yang, Yujun Shen, and Bolei Zhou · 2021
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Counterfactual zero-shot and open-set visual recognition
Zhongqi Yue, Tan Wang, Hanwang Zhang, Qianru Sun, and Xian-Sheng Hua · 2021
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Counterfactuals uncover the modular structure of deep generative models
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Momentum contrast for unsupervised visual representation learning
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Diffusion models already have a semantic latent space
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Unsupervised representation learning from pre-trained diffusion probabilistic models
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Consistency models
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