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The Potential of Autoregressive Models in Artificial Intelligence
Overview
In the realm of artificial intelligence, autoregressive (AR) large language models (LLMs) like the GPT series have made significant strides in achieving general artificial intelligence (AGI) through self-supervised learning.
Language Models
AR models, known for their scalability and generalizability, show promise in learning from vast amounts of unlabeled data, bringing us closer to AGI.
Computer Vision
In computer vision, efforts like VQGAN and DALL-E have demonstrated the potential of AR models in image generation, although further exploration of scaling laws is needed.
Visual AutoRegressive (VAR) Modeling
Researchers at Peking University have introduced VAR modeling, which leverages a multi-scale autoregressive paradigm to significantly improve AR baselines, especially in the ImageNet 256×256 benchmark.
Empirical Validation
Empirical validation of VAR models has revealed 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, empirical validation of scaling laws, and zero-shot generalization potential, aiming to bridge the gap between language models and computer vision.
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