Learning and Leveraging World Models in Visual Representation Learning
(2403.00504)Abstract
Joint-Embedding Predictive Architecture (JEPA) has emerged as a promising self-supervised approach that learns by leveraging a world model. While previously limited to predicting missing parts of an input, we explore how to generalize the JEPA prediction task to a broader set of corruptions. We introduce Image World Models, an approach that goes beyond masked image modeling and learns to predict the effect of global photometric transformations in latent space. We study the recipe of learning performant IWMs and show that it relies on three key aspects: conditioning, prediction difficulty, and capacity. Additionally, we show that the predictive world model learned by IWM can be adapted through finetuning to solve diverse tasks; a fine-tuned IWM world model matches or surpasses the performance of previous self-supervised methods. Finally, we show that learning with an IWM allows one to control the abstraction level of the learned representations, learning invariant representations such as contrastive methods, or equivariant representations such as masked image modelling.
Overview
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Introduces Image World Models (IWM) extending Joint-Embedding Predictive Architecture (JEPA) to predict global photometric transformations in latent space for improved self-supervised learning.
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Explores effective conditioning on transformations, the necessity of transformation complexity, and the capacity requirements for assimilating and using this information.
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Demonstrates that feature conditioning and complex transformations paired with sufficient model capacity enhance learning of effective world models.
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Suggests potential for multitask finetuning of predictors and the ability to adjust the level of representation abstraction, with implications for privacy, bias, and ethical use cases.
Learning and Leveraging World Models in Visual Representation Learning
Introduction
Recent advancements have spotlighted the efficacy of Joint-Embedding Predictive Architecture (JEPA) in self-supervised learning, a domain that entails predicting missing or corrupted parts of an input to learn useful representations. This paper introduces a novel approach, Image World Models (IWM), that extends the JEPA framework to predict the effects of global photometric transformations in latent space, offering a more generalized model of world understanding. Key to the study is the exploration of how to effectively condition these predictions on transformations, the necessary complexity of these transformations, and the capacity requirements for a predictive model to assimilate and utilize this information robustly.
Related Works
The landscape of self-supervised learning is rich and varied, with techniques ranging from augmentation-invariant approaches to more complex world modeling in visual representation. This paper’s contribution, IWM, is positioned within this spectrum, directly addressing the limitations of current augmentation-focused methods by enriching the model's predictive capabilities beyond mere mask filling tasks.
Methodology
The core mechanism underlying IWM is a refined approach to predicting transformations in latent space, which orchestrates a careful balance between the complexity of transformations, how the model is conditioned on these transformations, and the predictive capacity of the model. Through extensive experimentation, it was demonstrated that failing to adequately address any one of these aspects leads to suboptimal invariant representations, failing to leverage the rich potential of predictive modeling for visual representation learning.
Key Findings
- World Model Conditioning: Two distinct methods of conditioning the predictor on transformation information were evaluated, with feature conditioning emerging as the preferred method due to its superior downstream performance.
- Transformation Complexity and World Model Capacity: The study revealed that complex transformations, when paired with sufficient model capacity, considerably enhance the model's ability to learn effective world models.
- Predictor Finetuning: A standout conclusion from the study is the demonstrable effectiveness of finetuning the predictor for downstream tasks, suggesting that a finetuned world model can significantly outperform traditional encoder finetuning methods.
Implications and Future Directions
This work suggests several future explorations within the realm of visual representation learning. One significant implication is the potential for multitask finetuning of predictors, which could pave the way for even more flexible and efficient adaptation to varied downstream tasks. Furthermore, by controlling the capacity of world models, there is scope to manipulate the abstraction level of learned representations, offering a newfound adjustability in the tradeoff between adaptation ease and performance.
The concept of IWM presents a promising direction for future research, potentially unlocking more generalized and adaptable models for visual representation learning. However, it's crucial to remain mindful of the broader implications of increasingly powerful models, particularly regarding privacy, bias, and ethical use cases.
Conclusion
In conclusion, the development of Image World Models represents a significant step forward in our understanding and capabilities within the realm of self-supervised learning. By learning to predict a wide range of transformations in latent space, IWM not only enhances the model's predictive prowess but also provides a more versatile foundation for tackling diverse downstream tasks, effectively bridging the gap between invariant and equivariant world modeling in visual representation.learning.
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