mlsanityData to 7 Oct 2026

The Evolution and Future Perspectives of Artificial Intelligence Generated Content

arXiv 2412.01948 ↗First posted 2 Dec 2024cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Artificial intelligence generated content (AIGC), a rapidly advancing technology, is transforming content creation across domains, such as text, images, audio, and video. Its growing potential has attracted more and more researchers and investors to explore and expand its possibilities. This review traces AIGC's evolution through four developmental milestones-ranging from early rule-based systems to modern transfer learning models-within a unified framework that highlights how each milestone contributes uniquely to content generation. In particular, the paper employs a common example across all milestones to illustrate the capabilities and limitations of methods within each phase, providing a consistent evaluation of AIGC methodologies and their development. Furthermore, this paper addresses critical challenges associated with AIGC and proposes actionable strategies to mitigate them. This study aims to guide researchers and practitioners in selecting and optimizing AIGC models to enhance the quality and efficiency of content creation across diverse domains.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 76 of its 169 references that are in the map. First posted on arXiv 2 Dec 2024.

Builds on

76 of 169 references.

References · 76

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  4. 2020Language Models are Few-Shot Learnerscited by 32,199
  5. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  6. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  7. 2023GPT-4 Technical Reportcited by 18,132
  8. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  9. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  10. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  11. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  12. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
38 more
  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  3. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  4. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,581
  5. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  6. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,337
  7. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  8. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  9. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  10. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  11. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  12. 2014Conditional Generative Adversarial Netscited by 2,152
  13. 2016Adversarial examples in the physical worldcited by 1,762
  14. 2016Concrete Problems in AI Safetycited by 1,582
  15. 2023GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpointscited by 1,311
  16. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  17. 2022Magic3D: High-Resolution Text-to-3D Content Creationcited by 1,285
  18. 2014How transferable are features in deep neural networks?cited by 1,216
  19. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
  20. 2016Least Squares Generative Adversarial Networkscited by 1,000
  21. 2017Revisiting Unreasonable Effectiveness of Data in Deep Learning Eracited by 916
  22. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 858
  23. 2018Self-Attention Generative Adversarial Networkscited by 809
  24. 2019How multilingual is Multilingual BERT?cited by 642
  25. 2017In-Datacenter Performance Analysis of a Tensor Processing Unitcited by 610
  26. 2021Towards Personalized Federated Learningcited by 462
  27. 2017Certifying Some Distributional Robustness with Principled Adversarial Trainingcited by 333
  28. 2020Data and its (dis)contents: A survey of dataset development and use in machine learning researchcited by 259
  29. 2017Pose Guided Person Image Generationcited by 251
  30. 2023A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTcited by 221
  31. 2021A Survey on Neural Speech Synthesiscited by 182
  32. 2023RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3Dcited by 168
  33. 2020Adversarial Filters of Dataset Biasescited by 159
  34. 2023Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Modelscited by 152
  35. 2022Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Modelscited by 151
  36. 2024Continual Learning with Pre-Trained Models: A Surveycited by 151
  37. 2024Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Datacited by 143
  38. 2022A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Modelscited by 128

50 most cited of 76 All 76 (JSON)

Built on by

Not cited yet.