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  "author": "Kenan E. Ak1",
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  "author": "Kenan E. Ak1",
            
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  "defined_in": "https://doi.org/10.48550/arXiv.2007.09923",
            
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  "doi": "10.57702/7vvjcf89",
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  "doi": "10.57702/7vvjcf89",
            
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  "doi_date_published": "2024-12-02",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Ning Xu2",
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      "extra_author": "Ning Xu2",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    {
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    {
            
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      "extra_author": "Zhe Lin2",
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      "extra_author": "Zhe Lin2",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Yilin Wang2",
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      "extra_author": "Yilin Wang2",
            
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      "orcid": ""
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      "orcid": ""
            
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    }
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    {
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    {
            
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      "description": "",
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      "description": "",
            
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      "display_name": "Image Generation",
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      "display_name": "Image Generation",
            
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      "name": "image-generation",
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      "name": "image-generation",
            
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      "title": "Image Generation"
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      "title": "Image Generation"
            
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    }
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  ],
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  ],
            
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  "metadata_modified": "2024-12-02T18:25:30.361701",
            
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  "name": 
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  "name": 
            
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g-reinforced-adversarial-learning-in-autoregressive-image-generation",
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g-reinforced-adversarial-learning-in-autoregressive-image-generation",
            
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  "notes": "Autoregressive models recently achieved comparable results 
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  "notes": "Autoregressive models recently achieved comparable results 
            
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versus state-of-the-art Generative Adversarial Networks (GANs) with 
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versus state-of-the-art Generative Adversarial Networks (GANs) with 
            
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the help of Vector Quantized Variational AutoEncoders (VQ-VAE). 
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the help of Vector Quantized Variational AutoEncoders (VQ-VAE). 
            
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However, autoregressive models have several limitations such as 
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However, autoregressive models have several limitations such as 
            
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exposure bias and their training objective does not guarantee visual 
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exposure bias and their training objective does not guarantee visual 
            
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fidelity. To address these limitations, we propose to use Reinforced 
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fidelity. To address these limitations, we propose to use Reinforced 
            
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Adversarial Learning (RAL) based on policy gradient optimization for 
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Adversarial Learning (RAL) based on policy gradient optimization for 
            
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autoregressive models.",
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autoregressive models.",
            
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    {
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    {
            
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      "display_name": "Reinforced Adversarial Learning",
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      "display_name": "Reinforced Adversarial Learning",
            
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      "id": "cbb575d5-9126-4e15-a98f-53f1be7d0274",
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      "id": "cbb575d5-9126-4e15-a98f-53f1be7d0274",
            
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      "name": "Reinforced Adversarial Learning",
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      "name": "Reinforced Adversarial Learning",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
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    },
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    },
            
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    {
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    {
            
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      "display_name": "Vector Quantized Variational AutoEncoders",
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      "display_name": "Vector Quantized Variational AutoEncoders",
            
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      "id": "0b3611ec-a8bc-48cb-b02c-2d75741383f5",
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      "id": "0b3611ec-a8bc-48cb-b02c-2d75741383f5",
            
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      "name": "Vector Quantized Variational AutoEncoders",
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      "name": "Vector Quantized Variational AutoEncoders",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
            
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    }
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    }
            
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  ],
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  ],
            
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  "title": "Incorporating Reinforced Adversarial Learning in 
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  "title": "Incorporating Reinforced Adversarial Learning in 
            
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Autoregressive Image Generation",
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Autoregressive Image Generation",
            
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  "type": "dataset",
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  "type": "dataset",
            
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  "version": ""
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  "version": ""
            
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}
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}