“‘DALL·E 1’ Tag”,2019-09-12

Bibliography for tag ai/nn/transformer/gpt/dall-e/1, most recent first: 52 annotations & 1 link (parent).

• See Also
• Links
  • “Transfusion: Predict the Next Token and Diffuse Images With One Multi-Modal Model”, Zhou et al 2024
  • “JPEG-LM: LLMs As Image Generators With Canonical Codec Representations”, Han et al 2024
  • “MAR: Autoregressive Image Generation without Vector Quantization”, Li et al 2024
  • “STAR: Scale-Wise Text-To-Image Generation via Auto-Regressive Representations”, Ma et al 2024
  • “Chameleon: Mixed-Modal Early-Fusion Foundation Models”, Team2024
  • “Visual Autoregressive Modeling (VAR): Scalable Image Generation via Next-Scale Prediction”, Tian et al 2024
  • “IconShop: Text-Guided Vector Icon Synthesis With Autoregressive Transformers”, Wu et al 2023b
  • “Rejuvenating Image-GPT As Strong Visual Representation Learners”, Ren et al 2023
  • “Image Captioners Are Scalable Vision Learners Too”, Tschannen et al 2023
  • “Artificial Intelligence and Art: Identifying the Esthetic Judgment Factors That Distinguish Human & Machine-Generated Artwork”, Samo & Highhouse2023
  • “VALL-E: Neural Codec Language Models Are Zero-Shot Text to Speech Synthesizers”, Wang et al 2023
  • “Retrieval-Augmented Multimodal Language Modeling”, Yasunaga et al 2022
  • “Draft-And-Revise: Effective Image Generation With Contextual RQ-Transformer”, Lee et al 2022
  • “CogVideo: Large-Scale Pretraining for Text-To-Video Generation via Transformers”, Hong et al 2022
  • “CogView2: Faster and Better Text-To-Image Generation via Hierarchical Transformers”, Ding et al 2022
  • “MaskGIT: Masked Generative Image Transformer”, Chang et al 2022
  • “CM3: A Causal Masked Multimodal Model of the Internet”, Aghajanyan et al 2022
  • “ERNIE-ViLG: Unified Generative Pre-Training for Bidirectional Vision-Language Generation”, Zhang et al 2021
  • “Emojich—Zero-Shot Emoji Generation Using Russian Language: a Technical Report”, Shonenkov et al 2021
  • “LAFITE: Towards Language-Free Training for Text-To-Image Generation”, Zhou et al 2021
  • “NÜWA: Visual Synthesis Pre-Training for Neural VisUal World CreAtion”, Wu et al 2021
  • “L-Verse: Bidirectional Generation Between Image and Text”, Kim et al 2021
  • “Telling Creative Stories Using Generative Visual Aids”, Ali & Parikh2021
  • “Unifying Multimodal Transformer for Bi-Directional Image and Text Generation”, Huang et al 2021
  • “Illiterate DALL·E Learns to Compose”, Singh et al 2021
  • “What Users Want? WARHOL: A Generative Model for Recommendation”, Samaran et al 2021
  • “ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generation”, Zhu et al 2021
  • “Chinese AI Lab Challenges Google, OpenAI With a Model of 1.75 Trillion Parameters”, Du2021
  • “M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis”, Zhang et al 2021
  • “CogView: Mastering Text-To-Image Generation via Transformers”, Ding et al 2021
  • “GODIVA: Generating Open-DomaIn Videos from NAtural Descriptions”, Wu et al 2021
  • “VideoGPT: Video Generation Using VQ-VAE and Transformers”, Yan et al 2021
  • “China’s GPT-3? BAAI Introduces Superscale Intelligence Model ‘Wu Dao 1.0’: The Beijing Academy of Artificial Intelligence (BAAI) Releases Wu Dao 1.0, China’s First Large-Scale Pretraining Model.”, Synced2021
  • “Paint by Word”, Bau et al 2021
  • “Generating Images With Sparse Representations”, Nash et al 2021
  • “M6: A Chinese Multimodal Pretrainer”, Lin et al 2021
  • “DALL·E 1: Creating Images from Text: We’ve Trained a Neural Network Called DALL·E That Creates Images from Text Captions for a Wide Range of Concepts Expressible in Natural Language”, Ramesh et al 2021
  • “Taming Transformers for High-Resolution Image Synthesis”, Esser et al 2020
  • “Text-To-Image Generation Grounded by Fine-Grained User Attention”, Koh et al 2020
  • “X-LXMERT: Paint, Caption and Answer Questions With Multi-Modal Transformers”, Cho et al 2020
  • “IGPT: Generative Pretraining from Pixels”, Chen et al 2020
  • “Image GPT (iGPT): We Find That, Just As a Large Transformer Model Trained on Language Can Generate Coherent Text, the Same Exact Model Trained on Pixel Sequences Can Generate Coherent Image Completions and Samples”, Chen et al 2020
  • “The Messy, Secretive Reality behind OpenAI’s Bid to save the World: The AI Moonshot Was Founded in the Spirit of Transparency. This Is the inside Story of How Competitive Pressure Eroded That Idealism”, Hao2020
  • “Conceptual Captions: A Cleaned, Hypernymed, Image Alt-Text Dataset For Automatic Image Captioning”, Sharma et al 2018
  • “Image Transformer”, Parmar et al 2018
  • “VQ-VAE: Neural Discrete Representation Learning”, Oord et al 2017
  • “Categorical Reparameterization With Gumbel-Softmax”, Jang et al 2016
  • “The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables”, Maddison et al 2016
  • “Borisdayma/dalle-Mini: DALL·E-Mini”
  • “Kingnobro/IconShop: (SIGGRAPH Asia2023) Code of “IconShop: Text-Guided Vector Icon Synthesis With Autoregressive Transformers””
  • “IconShop”
  • “The Little Red Boat Story (Make-A-Scene): Our Own Model Was Used to Generate All the Images in the Story, by Providing a Text and Simple Sketch Input”
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