Scalable Pre-training of Large Autoregressive Image Models
(2401.08541)Abstract
This paper introduces AIM, a collection of vision models pre-trained with an autoregressive objective. These models are inspired by their textual counterparts, i.e., LLMs, and exhibit similar scaling properties. Specifically, we highlight two key findings: (1) the performance of the visual features scale with both the model capacity and the quantity of data, (2) the value of the objective function correlates with the performance of the model on downstream tasks. We illustrate the practical implication of these findings by pre-training a 7 billion parameter AIM on 2 billion images, that achieves 84.0% on ImageNet-1k with a frozen trunk. Interestingly, even at this scale, we observe no sign of saturation in performance, suggesting that AIM potentially represents a new frontier for training large-scale vision models. The pre-training of AIM is similar to the pre-training of LLMs, and does not require any image-specific strategy to stabilize the training at scale.
Overview
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The study introduces Autoregressive Image Models (AIM) inspired by LLMs pre-training techniques.
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AIM's performance increases with model size and dataset scale without observed saturation.
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Effective training of AIM doesn't require vision-specific stabilization techniques.
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AIM shows robust performance across 15 diverse image recognition benchmarks.
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The research suggests potential for further scaling of vision models using uncurated datasets.
Overview of Autoregressive Image Models
The progression of large-scale vision models has been prominently influenced by the guiding principles established in the natural language processing domain. In particular, the strategy of pre-training large neural networks to generate versatile features has translated into significant advancements. A recent study explore this approach by introducing a series of vision models, collectively known as Autoregressive Image Models (AIM), which are pre-trained using an autoregressive objective that mirrors the methodology applied in the development of LLMs.
Key Findings and Performance Scaling
AIM's core findings reveal two critical aspects: the model's performance scales in conjunction with its capacity and the quantity of data it is trained on, and the value of the objective function during training is a predictor for the model's effectiveness on downstream tasks. The research demonstrates these principles by training a model with 7 billion parameters on a dataset of 2 billion images. Remarkably, no saturation in performance improvement was detected, suggesting potential for future advancements in scaling up vision models.
Training Insights and Generalizability
Comprehensive analysis indicates that the success achieved by AIM does not necessitate specialized stabilization techniques specific to vision tasks. Instead, the AIM framework can be broadly applied, akin to the training processes of LLMs. Furthermore, evaluations across a diverse array of 15 image recognition benchmarks show that the AIM models attain robust performance, further supporting the utility of the autoregressive pre-training objective in preparing models for varied visual representations.
Conclusions and Future Directions
AIM's methodology represents a promising frontier in the development of large-scale vision models, offering a new perspective on the scalability and potential of leveraging extensive uncurated datasets. The absence of performance saturation points to a fertile ground for future exploration, where even more expansive models may be trained for extended periods to achieve unprecedented levels of visual understanding. The contributions of AIM position it as a landmark development, inspiring ongoing research in scalable and efficient vision models that can capitalize on the vast expanse of available imagery without predispositions towards specific visual content.
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