Autoregressive (AR) large language models (LLMs) like the GPT series are advancing general artificial intelligence (AGI) through self-supervised learning.
Language Models
AR models are scalable and can learn from massive amounts of data, moving us closer to AGI.
Computer Vision
Models like VQGAN and DALL-E have shown the potential of AR models in image generation, though further exploration of scaling laws is needed.
Visual AutoRegressive (VAR) Modeling
Peking University researchers introduced VAR modeling, which significantly enhances AR baselines, especially in the ImageNet 256×256 benchmark.
Empirical Validation
VAR models have promising scaling laws and zero-shot generalization capabilities, marking a breakthrough in visual autoregressive model performance.
Conclusion
The work introduces a new visual generative framework and aims to bridge the gap between language models and computer vision.
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